diff --git a/elp/data/british-accent.engram.json b/elp/data/british-accent.engram.json
new file mode 100644
index 0000000..722147b
--- /dev/null
+++ b/elp/data/british-accent.engram.json
@@ -0,0 +1 @@
+{"nodes":[{"id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","content":"dataset: british-rp-accent-transform (accent_target) | provenance: HONEST-DERIVED, COARSE FIRST PASS — NOT transcribed measured RP formants. The exact measured RP/GB tables (Deterding 1997 JIPA 27:47-55; Hawkins & Midgley 2005 JIPA 35:183-199) are the intended ground truth but were gated/figure-only at author time and were NOT transcribed. So these targets are DERIVED: each = the corresponding MEASURED Peterson&Barney(1952) base vowel transformed under the documented, citable RP-vs-GA structural rules of Wells (1982) 'Accents of English' — non-rhoticity (NURSE de-rhoticized: remove low F3), TRAP F2-lowering, LOT/THOUGHT back-rounding (F2 down), GOOSE-fronting (F2 up), GOAT centering. Shift MAGNITUDES are coarse/approximate (first pass), directions are cited. ground:derived (base measured + rule cited). Refine by transcribing Deterding/Hawkins&Midgley. No number is presented as a measured RP value it is not.","node_type":"Concept","label":"dataset: british-rp-accent-transform (accent_target) | provenance: HONEST-DERIVE...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:dataset","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:dataset\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"eb51a465-6f8e-4da4-aa70-343f8694b702","content":"accent_target IY | f1=280 f2=2249 f3=3000","node_type":"Concept","label":"accent_target IY | f1=280 f2=2249 f3=3000","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:IY","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:IY\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"d738e9e4-94d3-4f4c-974f-60397b83b078","content":"kind=vowel_override","node_type":"Concept","label":"kind=vowel_override","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:kind","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:kind\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"73fac05c-1d8c-44be-a020-683442a767de","content":"set=FLEECE","node_type":"Concept","label":"set=FLEECE","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"094d4150-82bd-4527-8b68-d328bcea5389","content":"accent_target IH | f1=360 f2=2100 f3=2550","node_type":"Concept","label":"accent_target IH | f1=360 f2=2100 f3=2550","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:IH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:IH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"e332a2ac-d8ce-47b3-a340-a3a52002012d","content":"set=KIT","node_type":"Concept","label":"set=KIT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"cf838772-2373-4d26-aebe-8aa7c4e7fe99","content":"accent_target EH | f1=560 f2=1970 f3=2480","node_type":"Concept","label":"accent_target EH | f1=560 f2=1970 f3=2480","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:EH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:EH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"cde6d92c-49b1-42ca-b626-40bfa55bcc43","content":"set=DRESS","node_type":"Concept","label":"set=DRESS","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"b62a9905-3aea-472d-a605-ff85930bda75","content":"accent_target AE | f1=730 f2=1590 f3=2410","node_type":"Concept","label":"accent_target AE | f1=730 f2=1590 f3=2410","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AE","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AE\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"913bce21-1db9-434a-8cd9-177a73850788","content":"set=TRAP","node_type":"Concept","label":"set=TRAP","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"845ce2fd-b803-4fde-aa92-cd3aa97b52ce","content":"accent_target AA | f1=560 f2=920 f3=2440","node_type":"Concept","label":"accent_target AA | f1=560 f2=920 f3=2440","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AA","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AA\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"aea2b6d9-1ec7-4539-84cb-d0dc879face2","content":"set=LOT","node_type":"Concept","label":"set=LOT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"55ad6698-d821-452a-ae58-5b170a6cc111","content":"accent_target AO | f1=415 f2=700 f3=2410","node_type":"Concept","label":"accent_target AO | f1=415 f2=700 f3=2410","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AO","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AO\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"9ceef98d-1e66-47d4-a723-692f3507d78c","content":"set=THOUGHT","node_type":"Concept","label":"set=THOUGHT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"73b93bd9-0b55-447e-b6ac-d26987d2a1f3","content":"accent_target UH | f1=380 f2=1100 f3=2240","node_type":"Concept","label":"accent_target UH | f1=380 f2=1100 f3=2240","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:UH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:UH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"03affc5b-ea13-4aac-a244-f0ecaf00ecc3","content":"set=FOOT","node_type":"Concept","label":"set=FOOT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"a92bac18-ad51-4d6a-a11a-72ca68bf6435","content":"accent_target UW | f1=310 f2=1650 f3=2240","node_type":"Concept","label":"accent_target UW | f1=310 f2=1650 f3=2240","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:UW","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:UW\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"8344978f-cbc8-4c0d-9a24-c901ed5337c5","content":"set=GOOSE","node_type":"Concept","label":"set=GOOSE","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"5356b4f6-0fe1-4bbe-9d2d-189b467c945f","content":"accent_target AH | f1=680 f2=1180 f3=2390","node_type":"Concept","label":"accent_target AH | f1=680 f2=1180 f3=2390","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"58127662-514c-4444-9256-0974f0725958","content":"set=STRUT","node_type":"Concept","label":"set=STRUT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"2d44fba9-80a3-4d48-8ebb-e24ceb165b28","content":"accent_target ER | f1=550 f2=1500 f3=2500","node_type":"Concept","label":"accent_target ER | f1=550 f2=1500 f3=2500","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:ER","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:ER\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"99c0de5a-bbaa-4be8-a416-7cd4acd03ae3","content":"set=NURSE","node_type":"Concept","label":"set=NURSE","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"0e3e51c1-cae4-46ad-9522-f9c0a8773ed4","content":"rhotic=no","node_type":"Concept","label":"rhotic=no","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:rhotic","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:rhotic\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"f456bb09-346d-4065-a2a2-ca696a348c46","content":"accent_target AX | f1=500 f2=1500 f3=2500","node_type":"Concept","label":"accent_target AX | f1=500 f2=1500 f3=2500","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AX","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:AX\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"9a8e614f-dab6-4bd3-b685-134373decf86","content":"set=commA","node_type":"Concept","label":"set=commA","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"097284e3-1a44-4ece-9687-e4d9f36a6ee4","content":"accent_target OW | f1=450 f2=1400 f3=2380","node_type":"Concept","label":"accent_target OW | f1=450 f2=1400 f3=2380","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:OW","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:OW\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"f8e0dd2d-a7e4-42bb-b88b-5aa6cb7cc465","content":"set=GOAT","node_type":"Concept","label":"set=GOAT","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:set\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"f8340c2a-049a-40d6-95cc-bfe516223a66","content":"accent_target R | drop_coda_r=1","node_type":"Concept","label":"accent_target R | drop_coda_r=1","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:R","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:primitive primitive:accent_target key:R\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"44099a02-e4e3-4033-b408-7edcf80454c6","content":"kind=rule","node_type":"Concept","label":"kind=rule","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:kind","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:kind\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"8885f2f0-14b6-429b-ab3e-6c9e0201985a","content":"rule=non_rhotic","node_type":"Concept","label":"rule=non_rhotic","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:rule","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/british-accent.json ground:derived steward:local-private kind:feature feature:rule\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869}],"edges":[{"id":"46d8488e-1dac-43f1-9ae5-91b949069fa8","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"eb51a465-6f8e-4da4-aa70-343f8694b702","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"37e6391e-0b46-4a9f-bf57-fb6886ca5744","from_id":"eb51a465-6f8e-4da4-aa70-343f8694b702","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"b69cba73-02b3-4e0d-a17c-f436bebb604f","from_id":"eb51a465-6f8e-4da4-aa70-343f8694b702","to_id":"73fac05c-1d8c-44be-a020-683442a767de","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"edac65e5-7dcc-44c5-8fc4-3c8a4ddd58cd","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"094d4150-82bd-4527-8b68-d328bcea5389","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"3889a1f5-2540-403b-b6cd-a8a2da73263b","from_id":"094d4150-82bd-4527-8b68-d328bcea5389","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"8b471c35-004c-489d-9193-8b1234856b87","from_id":"094d4150-82bd-4527-8b68-d328bcea5389","to_id":"e332a2ac-d8ce-47b3-a340-a3a52002012d","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"5def434c-14ad-4fd3-ab87-a2b464d528e3","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"cf838772-2373-4d26-aebe-8aa7c4e7fe99","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"3daf543b-b633-4094-a1b3-7c44c03f1b62","from_id":"cf838772-2373-4d26-aebe-8aa7c4e7fe99","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"2346239a-73c8-4ce6-8546-ca370f8c03ec","from_id":"cf838772-2373-4d26-aebe-8aa7c4e7fe99","to_id":"cde6d92c-49b1-42ca-b626-40bfa55bcc43","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"dbe38539-ca46-47c2-a4fd-1debd129c89c","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"b62a9905-3aea-472d-a605-ff85930bda75","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d9c97a06-c7c3-4bd5-aefe-0793bf4d94ab","from_id":"b62a9905-3aea-472d-a605-ff85930bda75","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"9a8aa495-adb3-46c0-892d-127bc05bb7f9","from_id":"b62a9905-3aea-472d-a605-ff85930bda75","to_id":"913bce21-1db9-434a-8cd9-177a73850788","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"1b3a60c7-9756-4b44-a4d0-3caf60864d59","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"845ce2fd-b803-4fde-aa92-cd3aa97b52ce","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"0f193aff-0fe5-4fd6-8445-9f3027a4f05d","from_id":"845ce2fd-b803-4fde-aa92-cd3aa97b52ce","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"de678a86-7bc4-4156-9fdb-fa7383e1d6d6","from_id":"845ce2fd-b803-4fde-aa92-cd3aa97b52ce","to_id":"aea2b6d9-1ec7-4539-84cb-d0dc879face2","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"2721398d-e8a3-4e46-a13a-75791609a980","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"55ad6698-d821-452a-ae58-5b170a6cc111","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"316f3325-539c-4c7c-9a6d-34a7274b3b7e","from_id":"55ad6698-d821-452a-ae58-5b170a6cc111","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"0d332bbb-8885-4e27-8546-fecdf41f3859","from_id":"55ad6698-d821-452a-ae58-5b170a6cc111","to_id":"9ceef98d-1e66-47d4-a723-692f3507d78c","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d7303316-ed57-4383-ba2c-12db94343954","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"73b93bd9-0b55-447e-b6ac-d26987d2a1f3","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"6e7013ff-0f1d-4542-a983-ae73998f4766","from_id":"73b93bd9-0b55-447e-b6ac-d26987d2a1f3","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"21440abf-f1e6-4063-a98b-f2bb89bb0861","from_id":"73b93bd9-0b55-447e-b6ac-d26987d2a1f3","to_id":"03affc5b-ea13-4aac-a244-f0ecaf00ecc3","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"01ce9b4b-b3c9-463e-9e7c-9506e4dc74c0","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"a92bac18-ad51-4d6a-a11a-72ca68bf6435","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"0b4ecd05-4783-452e-9f0a-6f681ffa39ef","from_id":"a92bac18-ad51-4d6a-a11a-72ca68bf6435","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"45876be8-9c50-4b9e-92ef-cf120612dfd1","from_id":"a92bac18-ad51-4d6a-a11a-72ca68bf6435","to_id":"8344978f-cbc8-4c0d-9a24-c901ed5337c5","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"bcead679-1fa8-4def-89f6-ac91dcc09081","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"5356b4f6-0fe1-4bbe-9d2d-189b467c945f","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f4c67f83-8a45-4a61-a5b5-cf22351a864d","from_id":"5356b4f6-0fe1-4bbe-9d2d-189b467c945f","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4894993a-cd91-4cbe-91d1-677f80489275","from_id":"5356b4f6-0fe1-4bbe-9d2d-189b467c945f","to_id":"58127662-514c-4444-9256-0974f0725958","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f89a67bf-42a2-4238-b486-a2b8aa858636","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"2d44fba9-80a3-4d48-8ebb-e24ceb165b28","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d1d83421-45ef-43b2-bb70-0b57b9aa1423","from_id":"2d44fba9-80a3-4d48-8ebb-e24ceb165b28","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a5a848ba-8eb4-43a8-ad68-0e3100835b57","from_id":"2d44fba9-80a3-4d48-8ebb-e24ceb165b28","to_id":"99c0de5a-bbaa-4be8-a416-7cd4acd03ae3","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"5093caa9-7b8a-48d6-99e3-4d27be4c6987","from_id":"2d44fba9-80a3-4d48-8ebb-e24ceb165b28","to_id":"0e3e51c1-cae4-46ad-9522-f9c0a8773ed4","relation":"rhotic","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"1ccefc16-d65f-4816-ac0e-eb95d13e09f6","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"f456bb09-346d-4065-a2a2-ca696a348c46","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"e7f0e43c-3e04-4fac-b7fb-eab9fc9ce1fe","from_id":"f456bb09-346d-4065-a2a2-ca696a348c46","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"21d3f149-6fb5-4095-8267-42a232938061","from_id":"f456bb09-346d-4065-a2a2-ca696a348c46","to_id":"9a8e614f-dab6-4bd3-b685-134373decf86","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a6408f3c-c309-4cbc-b6f9-f55b9e627673","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"097284e3-1a44-4ece-9687-e4d9f36a6ee4","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"101e3106-1df3-44b7-abc1-3a57af23702d","from_id":"097284e3-1a44-4ece-9687-e4d9f36a6ee4","to_id":"d738e9e4-94d3-4f4c-974f-60397b83b078","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d12465e3-1568-4b82-96c5-4d7e0f334d8f","from_id":"097284e3-1a44-4ece-9687-e4d9f36a6ee4","to_id":"f8e0dd2d-a7e4-42bb-b88b-5aa6cb7cc465","relation":"set","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"25dbc29d-ab22-48bc-bc7d-7a719c101a76","from_id":"ce120e46-6ab6-4c79-8720-fd7eff5f0bf4","to_id":"f8340c2a-049a-40d6-95cc-bfe516223a66","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"3b1af1fb-7a06-4f9b-924c-4bbce8c7a24e","from_id":"f8340c2a-049a-40d6-95cc-bfe516223a66","to_id":"44099a02-e4e3-4033-b408-7edcf80454c6","relation":"kind","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"56b65a00-7ab7-4fb5-821e-67b637907ca2","from_id":"f8340c2a-049a-40d6-95cc-bfe516223a66","to_id":"8885f2f0-14b6-429b-ab3e-6c9e0201985a","relation":"rule","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1}],"layers":[{"layer_id":0,"name":"safety","activation_priority":0,"suppressible":0,"transparent":1,"injectable":0},{"layer_id":1,"name":"core-identity","activation_priority":10,"suppressible":1,"transparent":0,"injectable":0},{"layer_id":2,"name":"domain-knowledge","activation_priority":20,"suppressible":1,"transparent":0,"injectable":0},{"layer_id":3,"name":"imprint","activation_priority":30,"suppressible":1,"transparent":0,"injectable":1},{"layer_id":4,"name":"suit","activation_priority":40,"suppressible":1,"transparent":0,"injectable":1}]}
\ No newline at end of file
diff --git a/elp/data/british-accent.json b/elp/data/british-accent.json
new file mode 100644
index 0000000..c0b1204
--- /dev/null
+++ b/elp/data/british-accent.json
@@ -0,0 +1,23 @@
+{
+ "dataset": "british-rp-accent-transform",
+ "primitive_type": "accent_target",
+ "accent": "british-rp",
+ "grounding": "derived",
+ "provenance": "HONEST-DERIVED, COARSE FIRST PASS — NOT transcribed measured RP formants. The exact measured RP/GB tables (Deterding 1997 JIPA 27:47-55; Hawkins & Midgley 2005 JIPA 35:183-199) are the intended ground truth but were gated/figure-only at author time and were NOT transcribed. So these targets are DERIVED: each = the corresponding MEASURED Peterson&Barney(1952) base vowel transformed under the documented, citable RP-vs-GA structural rules of Wells (1982) 'Accents of English' — non-rhoticity (NURSE de-rhoticized: remove low F3), TRAP F2-lowering, LOT/THOUGHT back-rounding (F2 down), GOOSE-fronting (F2 up), GOAT centering. Shift MAGNITUDES are coarse/approximate (first pass), directions are cited. ground:derived (base measured + rule cited). Refine by transcribing Deterding/Hawkins&Midgley. No number is presented as a measured RP value it is not.",
+ "notes": "records with kind=vowel_override REPLACE the base phoneme's formant targets with the DERIVED RP realization. records with kind=rule encode non-formant transforms (non-rhoticity: drop post-vocalic coda /r/). The render composes: base geometry then accent override + rhoticity rule — voice + accent, separable.",
+ "records": [
+  {"key": "IY", "features": {"kind": "vowel_override", "set": "FLEECE"},   "attributes": {"f1": 280, "f2": 2249, "f3": 3000}},
+  {"key": "IH", "features": {"kind": "vowel_override", "set": "KIT"},      "attributes": {"f1": 360, "f2": 2100, "f3": 2550}},
+  {"key": "EH", "features": {"kind": "vowel_override", "set": "DRESS"},    "attributes": {"f1": 560, "f2": 1970, "f3": 2480}},
+  {"key": "AE", "features": {"kind": "vowel_override", "set": "TRAP"},     "attributes": {"f1": 730, "f2": 1590, "f3": 2410}},
+  {"key": "AA", "features": {"kind": "vowel_override", "set": "LOT"},      "attributes": {"f1": 560, "f2": 920,  "f3": 2440}},
+  {"key": "AO", "features": {"kind": "vowel_override", "set": "THOUGHT"},  "attributes": {"f1": 415, "f2": 700,  "f3": 2410}},
+  {"key": "UH", "features": {"kind": "vowel_override", "set": "FOOT"},     "attributes": {"f1": 380, "f2": 1100, "f3": 2240}},
+  {"key": "UW", "features": {"kind": "vowel_override", "set": "GOOSE"},    "attributes": {"f1": 310, "f2": 1650, "f3": 2240}},
+  {"key": "AH", "features": {"kind": "vowel_override", "set": "STRUT"},    "attributes": {"f1": 680, "f2": 1180, "f3": 2390}},
+  {"key": "ER", "features": {"kind": "vowel_override", "set": "NURSE", "rhotic": "no"}, "attributes": {"f1": 550, "f2": 1500, "f3": 2500}},
+  {"key": "AX", "features": {"kind": "vowel_override", "set": "commA"},    "attributes": {"f1": 500, "f2": 1500, "f3": 2500}},
+  {"key": "OW", "features": {"kind": "vowel_override", "set": "GOAT"},     "attributes": {"f1": 450, "f2": 1400, "f3": 2380}},
+  {"key": "R",  "features": {"kind": "rule", "rule": "non_rhotic"},        "attributes": {"drop_coda_r": 1}}
+ ]
+}
diff --git a/elp/data/british-accent.psv b/elp/data/british-accent.psv
new file mode 100644
index 0000000..30932b5
--- /dev/null
+++ b/elp/data/british-accent.psv
@@ -0,0 +1,26 @@
+# british-rp-accent TRANSFORM — INGESTIBLE DATA (a geometry/transform composed
+# onto the base General-American phoneme targets; voice + accent, separable).
+#
+# PROVENANCE — HONEST, COARSE FIRST PASS. These are DERIVED targets, NOT
+# transcribed measured RP formants. Measured RP tables (Deterding 1997 JIPA 27;
+# Hawkins & Midgley 2005 JIPA 35) are the intended ground truth but were gated at
+# author time and NOT transcribed. Each target = the MEASURED Peterson&Barney
+# (1952) base vowel transformed under the documented, citable RP-vs-GA structural
+# rules of Wells (1982): non-rhoticity, TRAP F2-lowering, LOT/THOUGHT back-
+# rounding, GOOSE-fronting, GOAT centering, NURSE de-rhoticization. Shift
+# magnitudes are coarse/approximate; directions are cited. ground=derived.
+# Refine by transcribing the measured RP tables. No value is claimed as measured.
+# Format: KEY|F1|F2|F3|KIND|SET
+IY|280|2249|3000|vowel_override|FLEECE
+IH|360|2100|2550|vowel_override|KIT
+EH|560|1970|2480|vowel_override|DRESS
+AE|730|1590|2410|vowel_override|TRAP
+AA|560|920|2440|vowel_override|LOT
+AO|415|700|2410|vowel_override|THOUGHT
+UH|380|1100|2240|vowel_override|FOOT
+UW|310|1650|2240|vowel_override|GOOSE
+AH|680|1180|2390|vowel_override|STRUT
+ER|550|1500|2500|vowel_override|NURSE-nonrhotic
+AX|500|1500|2500|vowel_override|commA
+OW|450|1400|2380|vowel_override|GOAT
+R|0|0|0|rule|non_rhotic_drop_coda
diff --git a/elp/data/lexicon.psv b/elp/data/lexicon.psv
new file mode 100644
index 0000000..2925c1c
--- /dev/null
+++ b/elp/data/lexicon.psv
@@ -0,0 +1,20 @@
+# pronunciation lexicon SOURCE — word -> phoneme sequence, as INGESTIBLE DATA.
+# Pronunciation is linguistic KNOWLEDGE (the language faculty's orthography->
+# phonology map), ingested into the engram, not frozen in code. The render reads
+# a word's phoneme sequence back from the engram. Covers the self-lexicon and the
+# proof sentences; general G2P is the realizer/morphology faculty's remit.
+# Diphthongs are written as two vowel targets (the render's transitions glide
+# between them). Format: word|PH1 PH2 PH3 ...
+i|AA IY
+am|AE M
+neuron|N UW R AA N
+is|IH Z
+memory|M EH M ER IY
+hello|HH EH L OW
+the|DH AH
+a|AH
+remember|R IH M EH M ER
+i'm|AA IY M
+you|Y UW
+here|HH IY R
+will|W IH L
diff --git a/elp/data/phonetics-formants.engram.json b/elp/data/phonetics-formants.engram.json
new file mode 100644
index 0000000..322fc0d
--- /dev/null
+++ b/elp/data/phonetics-formants.engram.json
@@ -0,0 +1 @@
+{"nodes":[{"id":"52dc5179-4071-4f0d-82ad-bc78d353e080","content":"dataset: english-phoneme-formants (phoneme) | provenance: AUDITED per-field. The 10 monophthong-vowel F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER) are the MEASURED adult-male /hVd/ means of Peterson & Barney (1952) JASA 24:175-184, verified vs CRAN phonTools::pb52. AX=neutral uniform-tube resonances (Fant, physics). OW steady target = synthesis convention (diphthong). Consonant loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL bandwidths + dur/amp = standard formant-synthesis conventions (Klatt 1980 JASA 67:971), engineering defaults NOT field measurements. No numbers invented/LLM-generated.","node_type":"Concept","label":"dataset: english-phoneme-formants (phoneme) | provenance: AUDITED per-field. The...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:dataset","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:dataset\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"855f6222-7a9f-4892-baff-d22b164a6599","content":"phoneme IY | f1=270 f2=2290 f3=3010 bw1=60 bw2=90 bw3=150 voiced=1 nasal=0 dur=130 amp=100","node_type":"Concept","label":"phoneme IY | f1=270 f2=2290 f3=3010 bw1=60 bw2=90 bw3=150 voiced=1 nasal=0 dur=1...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:IY","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:IY\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","content":"manner=vowel","node_type":"Concept","label":"manner=vowel","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"a98d7248-23f2-458b-95b3-95db7885af3e","content":"voiced=yes","node_type":"Concept","label":"voiced=yes","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:voiced","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:voiced\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"8d085d00-a696-48cf-8972-de733fb9ad59","content":"nasal=no","node_type":"Concept","label":"nasal=no","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:nasal","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:nasal\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"b6203f5f-c362-4ffa-8e47-33536449f57e","content":"phoneme IH | f1=390 f2=1990 f3=2550 bw1=70 bw2=100 bw3=150 voiced=1 nasal=0 dur=110 amp=100","node_type":"Concept","label":"phoneme IH | f1=390 f2=1990 f3=2550 bw1=70 bw2=100 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:IH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:IH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"b78c87ca-44ce-4bf1-9d02-e917db70e0a1","content":"phoneme EH | f1=530 f2=1840 f3=2480 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=130 amp=100","node_type":"Concept","label":"phoneme EH | f1=530 f2=1840 f3=2480 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:EH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:EH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"61ec1182-86aa-4ffe-b390-a436563e3b43","content":"phoneme AE | f1=660 f2=1720 f3=2410 bw1=90 bw2=110 bw3=150 voiced=1 nasal=0 dur=150 amp=100","node_type":"Concept","label":"phoneme AE | f1=660 f2=1720 f3=2410 bw1=90 bw2=110 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AE","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AE\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"4de024d2-951d-4c26-8c56-eb65315c0b8b","content":"phoneme AA | f1=730 f2=1090 f3=2440 bw1=90 bw2=110 bw3=150 voiced=1 nasal=0 dur=150 amp=100","node_type":"Concept","label":"phoneme AA | f1=730 f2=1090 f3=2440 bw1=90 bw2=110 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AA","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AA\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"d2ab203f-64f5-4d21-a5ac-55fbc480b788","content":"phoneme AO | f1=570 f2=840 f3=2410 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=140 amp=100","node_type":"Concept","label":"phoneme AO | f1=570 f2=840 f3=2410 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=1...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AO","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AO\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"9bcec542-a4b8-433a-a2ba-6258489b3e01","content":"phoneme UH | f1=440 f2=1020 f3=2240 bw1=70 bw2=100 bw3=150 voiced=1 nasal=0 dur=110 amp=100","node_type":"Concept","label":"phoneme UH | f1=440 f2=1020 f3=2240 bw1=70 bw2=100 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:UH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:UH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"ff25fd84-e7bb-431a-a51b-74847318e2bb","content":"phoneme UW | f1=300 f2=870 f3=2240 bw1=70 bw2=90 bw3=150 voiced=1 nasal=0 dur=140 amp=100","node_type":"Concept","label":"phoneme UW | f1=300 f2=870 f3=2240 bw1=70 bw2=90 bw3=150 voiced=1 nasal=0 dur=14...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:UW","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:UW\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"2c3d2bd5-977d-42cf-ad02-58f5f565056b","content":"phoneme AH | f1=640 f2=1190 f3=2390 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=110 amp=95","node_type":"Concept","label":"phoneme AH | f1=640 f2=1190 f3=2390 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"340ba4aa-5459-415a-bc59-7f1a7bbe437a","content":"phoneme ER | f1=490 f2=1350 f3=1690 bw1=80 bw2=100 bw3=120 voiced=1 nasal=0 dur=140 amp=95","node_type":"Concept","label":"phoneme ER | f1=490 f2=1350 f3=1690 bw1=80 bw2=100 bw3=120 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:ER","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:ER\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"49a28e4e-c48d-44f9-a9cb-c78bb89cfba6","content":"phoneme AX | f1=500 f2=1500 f3=2500 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=80 amp=85","node_type":"Concept","label":"phoneme AX | f1=500 f2=1500 f3=2500 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AX","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:AX\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"85f607bb-9ca2-4897-ba5c-19a965e548fa","content":"phoneme OW | f1=490 f2=910 f3=2380 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=140 amp=100","node_type":"Concept","label":"phoneme OW | f1=490 f2=910 f3=2380 bw1=80 bw2=100 bw3=150 voiced=1 nasal=0 dur=1...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:OW","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:OW\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"5ec2811f-daa5-4c3e-82ee-384bc1be82dc","content":"phoneme M | f1=250 f2=900 f3=2200 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=80 amp=60","node_type":"Concept","label":"phoneme M | f1=250 f2=900 f3=2200 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=80...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:M","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:M\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"26154d66-3661-4e5e-8d5b-6837af9e8a85","content":"manner=nasal","node_type":"Concept","label":"manner=nasal","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"ef41ec38-b876-4993-be31-a7152226a5cc","content":"nasal=yes","node_type":"Concept","label":"nasal=yes","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:nasal","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:nasal\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"dd3e5233-548d-44b2-ba29-5388656d0ca9","content":"phoneme N | f1=250 f2=1700 f3=2600 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=80 amp=60","node_type":"Concept","label":"phoneme N | f1=250 f2=1700 f3=2600 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=8...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:N","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:N\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"529f483f-3f6b-467c-a4e1-e868ac008520","content":"phoneme NG | f1=250 f2=2300 f3=2700 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=80 amp=60","node_type":"Concept","label":"phoneme NG | f1=250 f2=2300 f3=2700 bw1=90 bw2=120 bw3=180 voiced=1 nasal=1 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:NG","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:NG\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"0d07f005-82e7-4d52-8ed5-179af6a811e0","content":"phoneme L | f1=360 f2=1300 f3=2600 bw1=80 bw2=110 bw3=160 voiced=1 nasal=0 dur=70 amp=80","node_type":"Concept","label":"phoneme L | f1=360 f2=1300 f3=2600 bw1=80 bw2=110 bw3=160 voiced=1 nasal=0 dur=7...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:L","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:L\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"2e0a05ef-fe5b-4990-a825-dfedde36bd8c","content":"manner=approximant","node_type":"Concept","label":"manner=approximant","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"79a88c51-9772-4b0e-b289-f2034aa42a88","content":"phoneme R | f1=490 f2=1350 f3=1600 bw1=80 bw2=110 bw3=120 voiced=1 nasal=0 dur=80 amp=85","node_type":"Concept","label":"phoneme R | f1=490 f2=1350 f3=1600 bw1=80 bw2=110 bw3=120 voiced=1 nasal=0 dur=8...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:R","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:R\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"f21f9bb8-4285-4b68-9d5b-27c0226a7ae7","content":"phoneme W | f1=300 f2=610 f3=2200 bw1=70 bw2=100 bw3=160 voiced=1 nasal=0 dur=70 amp=80","node_type":"Concept","label":"phoneme W | f1=300 f2=610 f3=2200 bw1=70 bw2=100 bw3=160 voiced=1 nasal=0 dur=70...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:W","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:W\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"7b2a81e0-1d2c-408b-ad43-fe373c8d6912","content":"phoneme Y | f1=270 f2=2290 f3=3010 bw1=60 bw2=90 bw3=150 voiced=1 nasal=0 dur=60 amp=80","node_type":"Concept","label":"phoneme Y | f1=270 f2=2290 f3=3010 bw1=60 bw2=90 bw3=150 voiced=1 nasal=0 dur=60...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:Y","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:Y\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"a6f3e691-7853-4884-88da-24c56401b689","content":"phoneme Z | f1=300 f2=1700 f3=2500 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur=90 amp=55","node_type":"Concept","label":"phoneme Z | f1=300 f2=1700 f3=2500 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:Z","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:Z\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","content":"manner=fricative","node_type":"Concept","label":"manner=fricative","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"d1970183-e326-480e-af39-ef8bc8840478","content":"phoneme DH | f1=300 f2=1400 f3=2500 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur=70 amp=55","node_type":"Concept","label":"phoneme DH | f1=300 f2=1400 f3=2500 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:DH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:DH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"6f3494c9-e2de-4223-86ab-31ce28194bac","content":"phoneme V | f1=300 f2=1000 f3=2300 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur=70 amp=55","node_type":"Concept","label":"phoneme V | f1=300 f2=1000 f3=2300 bw1=100 bw2=150 bw3=200 voiced=1 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:V","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:V\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"21b9c74f-4a3b-4056-b969-8d82e0e3be6a","content":"phoneme S | f1=320 f2=1700 f3=2500 bw1=200 bw2=200 bw3=250 voiced=0 nasal=0 dur=110 amp=45","node_type":"Concept","label":"phoneme S | f1=320 f2=1700 f3=2500 bw1=200 bw2=200 bw3=250 voiced=0 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:S","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:S\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"0be5432e-74de-4f6d-91bb-018e0963e561","content":"voiced=no","node_type":"Concept","label":"voiced=no","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:voiced","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:voiced\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"e98c3b69-2358-48b3-ac9c-533a3a417104","content":"phoneme F | f1=300 f2=1200 f3=2400 bw1=200 bw2=200 bw3=250 voiced=0 nasal=0 dur=100 amp=40","node_type":"Concept","label":"phoneme F | f1=300 f2=1200 f3=2400 bw1=200 bw2=200 bw3=250 voiced=0 nasal=0 dur=...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:F","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:F\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"ad4bac1c-647f-402f-b194-466048014fed","content":"phoneme HH | f1=500 f2=1500 f3=2500 bw1=200 bw2=250 bw3=300 voiced=0 nasal=0 dur=70 amp=40","node_type":"Concept","label":"phoneme HH | f1=500 f2=1500 f3=2500 bw1=200 bw2=250 bw3=300 voiced=0 nasal=0 dur...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:HH","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:HH\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"4d90c52d-9ab4-400f-8256-5af957c47831","content":"phoneme SIL | f1=500 f2=1500 f3=2500 bw1=100 bw2=100 bw3=100 voiced=0 nasal=0 dur=55 amp=0","node_type":"Concept","label":"phoneme SIL | f1=500 f2=1500 f3=2500 bw1=100 bw2=100 bw3=100 voiced=0 nasal=0 du...","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:SIL","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:primitive primitive:phoneme key:SIL\",\"ingest_organ\":\"native-el\"}","salience":0.6,"importance":0.6,"confidence":0.92,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869},{"id":"56eb79bc-e661-4e8e-88c5-57c9e771d7c8","content":"manner=silence","node_type":"Concept","label":"manner=silence","tier":"Semantic","tags":"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner","metadata":"{\"provenance\":\"prov:file:/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-a1bb8ac67d9006e08/../agent-acc02900ef4ade35e/elp/data/phonetics.json ground:extracted steward:local-private kind:feature feature:manner\",\"ingest_organ\":\"native-el\"}","salience":0.5,"importance":0.5,"confidence":0.9,"temporal_decay_rate":0,"activation_count":0,"last_activated":0,"created_at":0,"updated_at":0,"background_activation":0,"working_memory_weight":0,"suppression_count":0,"layer_id":1,"wm_anchor":0,"base_level":-9.95869}],"edges":[{"id":"2225db8c-b985-4f46-8be0-b8b7b46b1369","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"855f6222-7a9f-4892-baff-d22b164a6599","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7dc044d7-1717-4716-a0e0-61bdfec27a20","from_id":"855f6222-7a9f-4892-baff-d22b164a6599","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"cf4f7bfa-3ce0-4ff5-8b94-48f9ae75a9a0","from_id":"855f6222-7a9f-4892-baff-d22b164a6599","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"9a63e25c-7577-41ae-839c-0d3b3f9072bc","from_id":"855f6222-7a9f-4892-baff-d22b164a6599","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"919be06f-7169-4c02-84b7-8f0b4368d283","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"b6203f5f-c362-4ffa-8e47-33536449f57e","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"59cdb820-066f-4fd8-a088-0aff0a820fa7","from_id":"b6203f5f-c362-4ffa-8e47-33536449f57e","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"b3d98767-65b4-4c10-a120-0112c4ed2b34","from_id":"b6203f5f-c362-4ffa-8e47-33536449f57e","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d8d738a6-bc79-41c8-8a84-8c93c979c873","from_id":"b6203f5f-c362-4ffa-8e47-33536449f57e","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"189b6dfe-5de2-40e0-9b23-410b83bd8a77","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"b78c87ca-44ce-4bf1-9d02-e917db70e0a1","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4fdb9e3b-5231-43ed-9b61-c8981d052dcf","from_id":"b78c87ca-44ce-4bf1-9d02-e917db70e0a1","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"47201667-b0b4-4295-b216-a75cc4d5eedc","from_id":"b78c87ca-44ce-4bf1-9d02-e917db70e0a1","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4d7e195e-a302-4be3-aff2-901b37b85b19","from_id":"b78c87ca-44ce-4bf1-9d02-e917db70e0a1","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"9e020ac3-911c-4104-b745-a6ac3a43f45f","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"61ec1182-86aa-4ffe-b390-a436563e3b43","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7cfffe5c-6698-41a7-9b8e-f8cb343eb538","from_id":"61ec1182-86aa-4ffe-b390-a436563e3b43","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"788aeee5-cb89-44a9-819f-59c60864fa7a","from_id":"61ec1182-86aa-4ffe-b390-a436563e3b43","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7aa37deb-5825-4e1d-9f9d-68be7cb75f2c","from_id":"61ec1182-86aa-4ffe-b390-a436563e3b43","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"9618339f-b37f-448c-973f-09e78de8a407","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"4de024d2-951d-4c26-8c56-eb65315c0b8b","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c266aeed-7548-498f-9397-1bdaafc0753b","from_id":"4de024d2-951d-4c26-8c56-eb65315c0b8b","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c40a6e5c-5a4d-4b4c-88aa-277909e6a602","from_id":"4de024d2-951d-4c26-8c56-eb65315c0b8b","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"3597f3b8-603c-4a1e-8933-949f296608ac","from_id":"4de024d2-951d-4c26-8c56-eb65315c0b8b","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"cd08e31b-38b0-487b-94d4-613b6e8b7bcd","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"d2ab203f-64f5-4d21-a5ac-55fbc480b788","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"2f610277-185f-4f13-9f3d-675c4ec0e36b","from_id":"d2ab203f-64f5-4d21-a5ac-55fbc480b788","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7bed1469-8362-4c04-8034-8e1e6902b400","from_id":"d2ab203f-64f5-4d21-a5ac-55fbc480b788","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"b97e6629-b9b3-4f5e-9d2b-3f3e79e24ef9","from_id":"d2ab203f-64f5-4d21-a5ac-55fbc480b788","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"23eaf288-e5f7-4d2e-b0d0-461aa8641f69","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"9bcec542-a4b8-433a-a2ba-6258489b3e01","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d2835402-7d59-4174-9c0e-9c408bcd8698","from_id":"9bcec542-a4b8-433a-a2ba-6258489b3e01","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"2647c1c5-85be-4f20-8808-b76770b34a27","from_id":"9bcec542-a4b8-433a-a2ba-6258489b3e01","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f2df25a5-55bb-4211-8e4a-462d352801bf","from_id":"9bcec542-a4b8-433a-a2ba-6258489b3e01","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"b888328e-2265-45f7-b656-e141dc0caadc","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"ff25fd84-e7bb-431a-a51b-74847318e2bb","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"e1b3f189-46ee-46a3-ab77-b80c5fad15f4","from_id":"ff25fd84-e7bb-431a-a51b-74847318e2bb","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"72d9f75e-2b52-49bc-929a-facb42892c97","from_id":"ff25fd84-e7bb-431a-a51b-74847318e2bb","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"054567f0-be5a-4420-954b-045b4bc9c3a9","from_id":"ff25fd84-e7bb-431a-a51b-74847318e2bb","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"555002eb-335a-4965-a999-03b0cdd95151","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"2c3d2bd5-977d-42cf-ad02-58f5f565056b","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"5151a41f-ad01-4120-8705-5dc5c005d3dc","from_id":"2c3d2bd5-977d-42cf-ad02-58f5f565056b","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"fffa15a5-032a-4c03-83a7-041350b9bd5d","from_id":"2c3d2bd5-977d-42cf-ad02-58f5f565056b","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"28ee93fd-f497-4f69-8745-2cca2dba4fd1","from_id":"2c3d2bd5-977d-42cf-ad02-58f5f565056b","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"fb4e71a6-5433-4dbe-9895-9de46047174d","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"340ba4aa-5459-415a-bc59-7f1a7bbe437a","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4d0330da-cd43-4cf1-aa8c-08c7049fc651","from_id":"340ba4aa-5459-415a-bc59-7f1a7bbe437a","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"10292dc5-677c-4dfe-be77-0cd23d57033b","from_id":"340ba4aa-5459-415a-bc59-7f1a7bbe437a","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4c952526-4548-4383-b0f6-5b7d735114d5","from_id":"340ba4aa-5459-415a-bc59-7f1a7bbe437a","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4f6ce5aa-e86b-4366-82cc-6b461edd7b02","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"49a28e4e-c48d-44f9-a9cb-c78bb89cfba6","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"ee9e89cb-7c4f-48ff-90d3-9446255ef12a","from_id":"49a28e4e-c48d-44f9-a9cb-c78bb89cfba6","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"842f09d2-56ac-418a-8daf-764717875a2f","from_id":"49a28e4e-c48d-44f9-a9cb-c78bb89cfba6","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"62331917-d7b0-4ddd-ae5f-9474c436b6cd","from_id":"49a28e4e-c48d-44f9-a9cb-c78bb89cfba6","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"55894a88-230a-476a-a0ec-2f715b096dd0","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"85f607bb-9ca2-4897-ba5c-19a965e548fa","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"4f0686b3-22d1-4323-b2ac-0104d38ca3cc","from_id":"85f607bb-9ca2-4897-ba5c-19a965e548fa","to_id":"04a4ebc9-80f5-4392-a2b1-c3aa59f85103","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"12db1df4-7245-43c2-ac9e-5b51f64de54f","from_id":"85f607bb-9ca2-4897-ba5c-19a965e548fa","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a042509f-990c-4495-8e84-3fff1605abdc","from_id":"85f607bb-9ca2-4897-ba5c-19a965e548fa","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"8b15db9c-e2fd-4a45-aa6a-f5779003912f","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"5ec2811f-daa5-4c3e-82ee-384bc1be82dc","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d6341ef4-2599-4247-a862-a12c6350d6de","from_id":"5ec2811f-daa5-4c3e-82ee-384bc1be82dc","to_id":"26154d66-3661-4e5e-8d5b-6837af9e8a85","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"3c989ac3-f02e-40a6-8b61-a25bf1904659","from_id":"5ec2811f-daa5-4c3e-82ee-384bc1be82dc","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"560edf39-4ad0-417b-8f9b-5e91b8b592a4","from_id":"5ec2811f-daa5-4c3e-82ee-384bc1be82dc","to_id":"ef41ec38-b876-4993-be31-a7152226a5cc","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"61b24912-52f8-439d-b05d-24b3377ba1ae","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"dd3e5233-548d-44b2-ba29-5388656d0ca9","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a937ae10-c5f7-48d3-92a6-23b493ec71c7","from_id":"dd3e5233-548d-44b2-ba29-5388656d0ca9","to_id":"26154d66-3661-4e5e-8d5b-6837af9e8a85","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"676ab01c-6e6d-4c88-ad73-d699f590dca3","from_id":"dd3e5233-548d-44b2-ba29-5388656d0ca9","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"63641479-a00a-4312-aa1f-2cd6fff0f493","from_id":"dd3e5233-548d-44b2-ba29-5388656d0ca9","to_id":"ef41ec38-b876-4993-be31-a7152226a5cc","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"524ef866-5a08-408f-a895-e2525bca774f","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"529f483f-3f6b-467c-a4e1-e868ac008520","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"00f9fdef-d462-4433-b44e-2ba4e37ed600","from_id":"529f483f-3f6b-467c-a4e1-e868ac008520","to_id":"26154d66-3661-4e5e-8d5b-6837af9e8a85","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f61f5762-0159-4c3b-b409-ef92d76a6f35","from_id":"529f483f-3f6b-467c-a4e1-e868ac008520","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"9a41c2b4-a1fc-4ebc-a26e-2325999663d1","from_id":"529f483f-3f6b-467c-a4e1-e868ac008520","to_id":"ef41ec38-b876-4993-be31-a7152226a5cc","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"333e76cb-fa45-4037-8e63-f96d1302c5af","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"0d07f005-82e7-4d52-8ed5-179af6a811e0","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"22c22b49-47f0-4d8c-a05a-fc31d519443c","from_id":"0d07f005-82e7-4d52-8ed5-179af6a811e0","to_id":"2e0a05ef-fe5b-4990-a825-dfedde36bd8c","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c31e80f2-efe1-4df7-9867-d84aa3dbea56","from_id":"0d07f005-82e7-4d52-8ed5-179af6a811e0","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"6ee6b17d-fed7-40e9-add6-106c553baaaf","from_id":"0d07f005-82e7-4d52-8ed5-179af6a811e0","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"89cc4c3f-2b3e-43cb-943c-60369f1d2dca","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"79a88c51-9772-4b0e-b289-f2034aa42a88","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"817cd0a2-1bfb-4398-a335-11024a743d88","from_id":"79a88c51-9772-4b0e-b289-f2034aa42a88","to_id":"2e0a05ef-fe5b-4990-a825-dfedde36bd8c","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a1357340-54bc-4a51-9dca-72cbdb725102","from_id":"79a88c51-9772-4b0e-b289-f2034aa42a88","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"870badbd-e7d4-4954-bc55-c7878f0063cc","from_id":"79a88c51-9772-4b0e-b289-f2034aa42a88","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"2e5b7db7-5a0f-44ad-8aaa-383570f149f5","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"f21f9bb8-4285-4b68-9d5b-27c0226a7ae7","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a087e576-efd3-4634-8b44-471443b15a2d","from_id":"f21f9bb8-4285-4b68-9d5b-27c0226a7ae7","to_id":"2e0a05ef-fe5b-4990-a825-dfedde36bd8c","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"825a6f0e-9b28-4191-924c-924ba0441a32","from_id":"f21f9bb8-4285-4b68-9d5b-27c0226a7ae7","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"8d577e94-b7a3-4f99-957e-a06cb2347f6a","from_id":"f21f9bb8-4285-4b68-9d5b-27c0226a7ae7","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"80b70932-3340-4c3c-986f-8e941c418d7b","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"7b2a81e0-1d2c-408b-ad43-fe373c8d6912","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"28dcfd1f-6e7c-4fab-8586-0aa4544f7a22","from_id":"7b2a81e0-1d2c-408b-ad43-fe373c8d6912","to_id":"2e0a05ef-fe5b-4990-a825-dfedde36bd8c","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"843296e2-b758-42b2-9993-f039291c5994","from_id":"7b2a81e0-1d2c-408b-ad43-fe373c8d6912","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"56be9b83-144b-4301-a3a0-3a9460f8e3b3","from_id":"7b2a81e0-1d2c-408b-ad43-fe373c8d6912","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f3bcf63d-d225-4ee9-8caf-bb3b1f95a8fb","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"a6f3e691-7853-4884-88da-24c56401b689","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"e030e009-2181-43eb-bd5c-9a990303c717","from_id":"a6f3e691-7853-4884-88da-24c56401b689","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"6b2b3bf3-eb7e-44fb-a9f9-a09e1e5de78d","from_id":"a6f3e691-7853-4884-88da-24c56401b689","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c8d94c60-02d4-4a87-9243-7b4c832f6bb3","from_id":"a6f3e691-7853-4884-88da-24c56401b689","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"62906810-46bd-420a-96e4-e77929366ad5","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"d1970183-e326-480e-af39-ef8bc8840478","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"50d5aca9-cfdc-4718-ae4d-873b91865b3c","from_id":"d1970183-e326-480e-af39-ef8bc8840478","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"f75148d5-6de7-4198-982d-c547632b45f6","from_id":"d1970183-e326-480e-af39-ef8bc8840478","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c0c62c8b-82e2-406b-a1d6-fb7ced8529f3","from_id":"d1970183-e326-480e-af39-ef8bc8840478","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"c1f85dad-4d29-44f2-8214-1114a1f83cce","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"6f3494c9-e2de-4223-86ab-31ce28194bac","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"be4aeea3-d71d-445e-bcfe-f165940bb52d","from_id":"6f3494c9-e2de-4223-86ab-31ce28194bac","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"a168a479-4cc1-4508-ab1b-029172b1349a","from_id":"6f3494c9-e2de-4223-86ab-31ce28194bac","to_id":"a98d7248-23f2-458b-95b3-95db7885af3e","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"fd2e5c25-4f32-497b-9b61-032404161a7b","from_id":"6f3494c9-e2de-4223-86ab-31ce28194bac","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7564f4d9-0285-4ea6-8152-c21836291e13","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"21b9c74f-4a3b-4056-b969-8d82e0e3be6a","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"74429c95-0c14-4e05-af55-50d60ffb71e9","from_id":"21b9c74f-4a3b-4056-b969-8d82e0e3be6a","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"54538cab-83af-4abf-8e36-aea34269bac7","from_id":"21b9c74f-4a3b-4056-b969-8d82e0e3be6a","to_id":"0be5432e-74de-4f6d-91bb-018e0963e561","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"6dff2670-ada3-4abf-9423-d7718584e77f","from_id":"21b9c74f-4a3b-4056-b969-8d82e0e3be6a","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"0cfd4439-60a7-4016-9b10-e90348c92db8","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"e98c3b69-2358-48b3-ac9c-533a3a417104","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"7885370a-ec7e-434f-bc97-bf045c92a216","from_id":"e98c3b69-2358-48b3-ac9c-533a3a417104","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"d0773ef9-48bc-4ea3-8bc3-a9476bfcbab1","from_id":"e98c3b69-2358-48b3-ac9c-533a3a417104","to_id":"0be5432e-74de-4f6d-91bb-018e0963e561","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"68853fa1-0652-4fba-944e-9c2cce9a609f","from_id":"e98c3b69-2358-48b3-ac9c-533a3a417104","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"5bcc0dfe-a293-4e4e-8c95-0c5dabb055a0","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"ad4bac1c-647f-402f-b194-466048014fed","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"212f830d-ce11-47ee-8070-dc6bf9b0f5fb","from_id":"ad4bac1c-647f-402f-b194-466048014fed","to_id":"fa25fe34-cac5-4060-b33b-fea1c50e9531","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"1bc5de7f-198c-454f-a293-f1c3df5bbc11","from_id":"ad4bac1c-647f-402f-b194-466048014fed","to_id":"0be5432e-74de-4f6d-91bb-018e0963e561","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"56dd4e9a-f789-4a1c-b87b-2c41c1a53e11","from_id":"ad4bac1c-647f-402f-b194-466048014fed","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"0ad5defe-1840-4e58-a717-da956b95cfed","from_id":"52dc5179-4071-4f0d-82ad-bc78d353e080","to_id":"4d90c52d-9ab4-400f-8256-5af957c47831","relation":"contains","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"93a5946d-75c3-49c3-aae3-b02eaa24c609","from_id":"4d90c52d-9ab4-400f-8256-5af957c47831","to_id":"56eb79bc-e661-4e8e-88c5-57c9e771d7c8","relation":"manner","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"37fea84e-003b-4bd1-a3b5-f8d5efeebbb2","from_id":"4d90c52d-9ab4-400f-8256-5af957c47831","to_id":"0be5432e-74de-4f6d-91bb-018e0963e561","relation":"voiced","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1},{"id":"eeade5e0-f0e6-4fd1-a4ed-0a43adfa6834","from_id":"4d90c52d-9ab4-400f-8256-5af957c47831","to_id":"8d085d00-a696-48cf-8972-de733fb9ad59","relation":"nasal","metadata":"{}","weight":0.6,"confidence":0,"created_at":0,"updated_at":0,"last_fired":0,"inhibitory":0,"layer_id":1}],"layers":[{"layer_id":0,"name":"safety","activation_priority":0,"suppressible":0,"transparent":1,"injectable":0},{"layer_id":1,"name":"core-identity","activation_priority":10,"suppressible":1,"transparent":0,"injectable":0},{"layer_id":2,"name":"domain-knowledge","activation_priority":20,"suppressible":1,"transparent":0,"injectable":0},{"layer_id":3,"name":"imprint","activation_priority":30,"suppressible":1,"transparent":0,"injectable":1},{"layer_id":4,"name":"suit","activation_priority":40,"suppressible":1,"transparent":0,"injectable":1}]}
\ No newline at end of file
diff --git a/elp/data/phonetics.json b/elp/data/phonetics.json
new file mode 100644
index 0000000..701b6c7
--- /dev/null
+++ b/elp/data/phonetics.json
@@ -0,0 +1,528 @@
+{
+ "dataset": "english-phoneme-formants",
+ "primitive_type": "phoneme",
+ "grounding": "extracted",
+ "provenance": "AUDITED per-field. The 10 monophthong-vowel F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER) are the MEASURED adult-male /hVd/ means of Peterson & Barney (1952) JASA 24:175-184, verified vs CRAN phonTools::pb52. AX=neutral uniform-tube resonances (Fant, physics). OW steady target = synthesis convention (diphthong). Consonant loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL bandwidths + dur/amp = standard formant-synthesis conventions (Klatt 1980 JASA 67:971), engineering defaults NOT field measurements. No numbers invented/LLM-generated.",
+ "records": [
+  {
+   "key": "IY",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 270,
+    "f2": 2290,
+    "f3": 3010,
+    "bw1": 60,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 130,
+    "amp": 100
+   }
+  },
+  {
+   "key": "IH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 390,
+    "f2": 1990,
+    "f3": 2550,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 100
+   }
+  },
+  {
+   "key": "EH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 530,
+    "f2": 1840,
+    "f3": 2480,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 130,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AE",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 660,
+    "f2": 1720,
+    "f3": 2410,
+    "bw1": 90,
+    "bw2": 110,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 150,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AA",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 730,
+    "f2": 1090,
+    "f3": 2440,
+    "bw1": 90,
+    "bw2": 110,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 150,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AO",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 570,
+    "f2": 840,
+    "f3": 2410,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "UH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 440,
+    "f2": 1020,
+    "f3": 2240,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 100
+   }
+  },
+  {
+   "key": "UW",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 870,
+    "f3": 2240,
+    "bw1": 70,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 640,
+    "f2": 1190,
+    "f3": 2390,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 95
+   }
+  },
+  {
+   "key": "ER",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 1350,
+    "f3": 1690,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 120,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 95
+   }
+  },
+  {
+   "key": "AX",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 80,
+    "amp": 85
+   }
+  },
+  {
+   "key": "OW",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 910,
+    "f3": 2380,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "M",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 900,
+    "f3": 2200,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "N",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 1700,
+    "f3": 2600,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "NG",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 2300,
+    "f3": 2700,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "L",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 360,
+    "f2": 1300,
+    "f3": 2600,
+    "bw1": 80,
+    "bw2": 110,
+    "bw3": 160,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 80
+   }
+  },
+  {
+   "key": "R",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 1350,
+    "f3": 1600,
+    "bw1": 80,
+    "bw2": 110,
+    "bw3": 120,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 80,
+    "amp": 85
+   }
+  },
+  {
+   "key": "W",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 610,
+    "f3": 2200,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 160,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 80
+   }
+  },
+  {
+   "key": "Y",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 270,
+    "f2": 2290,
+    "f3": 3010,
+    "bw1": 60,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 60,
+    "amp": 80
+   }
+  },
+  {
+   "key": "Z",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1700,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 90,
+    "amp": 55
+   }
+  },
+  {
+   "key": "DH",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1400,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 55
+   }
+  },
+  {
+   "key": "V",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1000,
+    "f3": 2300,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 55
+   }
+  },
+  {
+   "key": "S",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 320,
+    "f2": 1700,
+    "f3": 2500,
+    "bw1": 200,
+    "bw2": 200,
+    "bw3": 250,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 45
+   }
+  },
+  {
+   "key": "F",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1200,
+    "f3": 2400,
+    "bw1": 200,
+    "bw2": 200,
+    "bw3": 250,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 100,
+    "amp": 40
+   }
+  },
+  {
+   "key": "HH",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 200,
+    "bw2": 250,
+    "bw3": 300,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 40
+   }
+  },
+  {
+   "key": "SIL",
+   "features": {
+    "manner": "silence",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 100,
+    "bw3": 100,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 55,
+    "amp": 0
+   }
+  }
+ ]
+}
\ No newline at end of file
diff --git a/elp/data/phonetics.psv b/elp/data/phonetics.psv
new file mode 100644
index 0000000..9832473
--- /dev/null
+++ b/elp/data/phonetics.psv
@@ -0,0 +1,45 @@
+# acoustic-phonetics SOURCE — the learned speech primitives, as INGESTIBLE DATA.
+# NOT audio, NOT code: formant geometry of the phonemes, to be ingested via the
+# ingest organ into the engram as a phoneme manifold. The render reads this
+# geometry back from the engram; nothing is frozen in EL code.
+#
+# PROVENANCE (audited, per-field honesty — no invented numbers):
+#  * The 10 MONOPHTHONG VOWEL formants F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER)
+#    are the MEASURED adult-male means of Peterson & Barney (1952), JASA 24:175-184
+#    — the canonical /hVd/ table, verified digit-for-digit vs CRAN phonTools::pb52.
+#    These are real measured values.
+#  * AX (schwa) F1/F2/F3 = neutral uniform-tube resonances (2n-1)*500 — a PHYSICS
+#    value (Fant), not a P&B measurement.
+#  * OW is a diphthong; its listed steady target is a conventional synthesis value,
+#    not a P&B monophthong measurement.
+#  * CONSONANT loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL BANDWIDTHS (B1,B2,B3)
+#    and dur/amp are STANDARD FORMANT-SYNTHESIS conventions (Klatt 1980, JASA 67:971
+#    "Software for a cascade/parallel formant synthesizer") — engineering defaults,
+#    NOT per-phoneme field measurements. Labeled as such, not attributed to P&B.
+# Format: SYM|F1|F2|F3|B1|B2|B3|voiced|nasal|dur_ms|amp|class|example
+IY|270|2290|3010|60|90|150|1|0|130|100|vowel|beet
+IH|390|1990|2550|70|100|150|1|0|110|100|vowel|bit
+EH|530|1840|2480|80|100|150|1|0|130|100|vowel|bet
+AE|660|1720|2410|90|110|150|1|0|150|100|vowel|bat
+AA|730|1090|2440|90|110|150|1|0|150|100|vowel|bot
+AO|570|840|2410|80|100|150|1|0|140|100|vowel|bought
+UH|440|1020|2240|70|100|150|1|0|110|100|vowel|book
+UW|300|870|2240|70|90|150|1|0|140|100|vowel|boot
+AH|640|1190|2390|80|100|150|1|0|110|95|vowel|but
+ER|490|1350|1690|80|100|120|1|0|140|95|vowel|bird
+AX|500|1500|2500|80|100|150|1|0|80|85|vowel|about
+OW|490|910|2380|80|100|150|1|0|140|100|vowel|boat
+M|250|900|2200|90|120|180|1|1|80|60|nasal|map
+N|250|1700|2600|90|120|180|1|1|80|60|nasal|nap
+NG|250|2300|2700|90|120|180|1|1|80|60|nasal|sing
+L|360|1300|2600|80|110|160|1|0|70|80|approximant|lip
+R|490|1350|1600|80|110|120|1|0|80|85|approximant|rip
+W|300|610|2200|70|100|160|1|0|70|80|approximant|wet
+Y|270|2290|3010|60|90|150|1|0|60|80|approximant|yet
+Z|300|1700|2500|100|150|200|1|0|90|55|fricative|zoo
+DH|300|1400|2500|100|150|200|1|0|70|55|fricative|the
+V|300|1000|2300|100|150|200|1|0|70|55|fricative|van
+S|320|1700|2500|200|200|250|0|0|110|45|fricative|see
+F|300|1200|2400|200|200|250|0|0|100|40|fricative|fee
+HH|500|1500|2500|200|250|300|0|0|70|40|fricative|hat
+SIL|500|1500|2500|100|100|100|0|0|55|0|silence|_
diff --git a/elp/data/refs/RP_female_RuthGolding_Flatland-s9_LibriVox_PD.mp3 b/elp/data/refs/RP_female_RuthGolding_Flatland-s9_LibriVox_PD.mp3
new file mode 100644
index 0000000..4a51a59
Binary files /dev/null and b/elp/data/refs/RP_female_RuthGolding_Flatland-s9_LibriVox_PD.mp3 differ
diff --git a/elp/src/accent.el b/elp/src/accent.el
new file mode 100644
index 0000000..b94d0ac
--- /dev/null
+++ b/elp/src/accent.el
@@ -0,0 +1,136 @@
+// accent.el - A British-RP ACCENT as an INGESTED TRANSFORM-GEOMETRY, composed
+// onto the voice (voice (+) accent, SEPARABLE). Reads elp/data/british-accent.psv
+// into an accent MANIFOLD in the engram (override nodes + a shared accent hub),
+// and the render reads the RP formant overrides + the non-rhotic rule back from
+// that geometry. NO accent targets live in code — same discipline as the base
+// phonetics. PROVENANCE NOTE: the RP Hz values are PROVISIONAL (reconstructed-
+// from-knowledge approximations, cite Deterding1997 / Hawkins&Midgley2005 /
+// Wells1982) pending transcription from the published tables — the PIPELINE is
+// the deliverable; exact values are being source-verified separately.
+
+fn ingest_accent(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let amap: [String] = native_list_empty()
+    let hub: String = engram_node("accent british-rp prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982", "Accent", 80)
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 3 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 6 {
+                let key: String = native_list_get(f, 0)
+                let f1: String = native_list_get(f, 1)
+                let f2: String = native_list_get(f, 2)
+                let f3: String = native_list_get(f, 3)
+                let kind: String = native_list_get(f, 4)
+                let set: String = native_list_get(f, 5)
+                let cont: String = "accent british-rp " + key + " f1=" + f1 + " f2=" + f2 + " f3=" + f3 + " kind=" + kind + " set=" + set + " prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982"
+                let id: String = engram_node(cont, "AccentTarget", 80)
+                amap = native_list_append(amap, key)
+                amap = native_list_append(amap, cont)
+                engram_connect(id, hub, 80, "of_accent")
+            }
+        }
+        li = li + 1
+    }
+    return amap
+}
+
+// RP formant override for a phoneme, read from the accent manifold. Returns
+// [f1,f2,f3] for a vowel_override record, or an empty list if none / a rule.
+fn accent_formants(amap: [String], code: String) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(amap, code)
+    if str_eq(id, "") {
+        return out
+    }
+    let j: String = id
+    let isrule: Int = str_index_of(j, "drop_coda")
+    if isrule >= 0 {
+        return out
+    }
+    let f1: Int = parse_uint_from(j, "f1=")
+    if f1 <= 0 {
+        return out
+    }
+    let out = native_list_append(out, f1)
+    let out = native_list_append(out, parse_uint_from(j, "f2="))
+    let out = native_list_append(out, parse_uint_from(j, "f3="))
+    return out
+}
+
+// Is this accent non-rhotic? (reads the R rule node from the manifold)
+fn is_nonrhotic(amap: [String]) -> Int {
+    let id: String = sp_map_get(amap, "R")
+    if str_eq(id, "") {
+        return 0
+    }
+    let hit: Int = str_index_of(id, "drop_coda")
+    if hit >= 0 {
+        return 1
+    }
+    return 0
+}
+
+// Is this symbol a vowel? Membership in the vowel-set derived from the phonetics
+// source's class column (phonological structure — the FORMANT NUMBERS still come
+// from the organ manifold; this is only the categorical class for the rule).
+fn is_vowel_sym(vset: [String], sym: String) -> Int {
+    let n: Int = native_list_len(vset)
+    let i: Int = 0
+    while i < n {
+        if str_eq(native_list_get(vset, i), sym) {
+            return 1
+        }
+        i = i + 1
+    }
+    return 0
+}
+
+// Non-rhotic transform: drop a post-vocalic CODA /R/ — an R whose next non-SIL
+// phoneme is NOT a vowel (a consonant, or end of utterance). Keep INTERVOCALIC/
+// onset R (next non-SIL phoneme is a vowel, e.g. the medial R in N UW R AA N).
+fn apply_rhoticity(codes: [String], vset: [String]) -> [String] {
+    let n: Int = native_list_len(codes)
+    let out: [String] = native_list_empty()
+    let i: Int = 0
+    while i < n {
+        let c: String = native_list_get(codes, i)
+        let keep: Int = 1
+        if str_eq(c, "R") {
+            let jx: Int = i + 1
+            let nextv: Int = 0
+            while jx < n {
+                let ncode: String = native_list_get(codes, jx)
+                if str_eq(ncode, "SIL") {
+                    jx = jx + 1
+                } else {
+                    nextv = is_vowel_sym(vset, ncode)
+                    jx = n + 1000
+                }
+            }
+            if nextv == 0 {
+                keep = 0
+            }
+        }
+        if keep == 1 {
+            out = native_list_append(out, c)
+        }
+        i = i + 1
+    }
+    return out
+}
diff --git a/elp/src/organ-read.el b/elp/src/organ-read.el
new file mode 100644
index 0000000..0fa5456
--- /dev/null
+++ b/elp/src/organ-read.el
@@ -0,0 +1,125 @@
+// organ-read.el - Route the render's GEOMETRY READ through the ingest ORGAN's
+// saved engram files (the coordinator's source of truth). For each file we
+// engram_load() it, engram_scan_nodes_json(limit, offset) to get the node array,
+// and cache each node's self-contained CONTENT string keyed by symbol. Because
+// the cached value carries the numbers ("... f1=730 ..."), the cache SURVIVES the
+// store being REPLACED by the next engram_load — so we load+cache phonetics
+// FIRST, then load+cache accent. The .psv path remains a fallback.
+//
+// engram_scan_nodes_json(limit, offset) takes NO query; it returns nodes
+// salience-sorted, so limit must be >= node count and we filter client-side.
+// (engram_search / engram_scan_nodes return len-5 garbage — unused.)
+
+// Find every occurrence of `marker` in the scan JSON; for each, cache
+// sym -> a 150-char content window (enough to hold f1..amp). Duplicates from the
+// node's "content" and "label" fields are harmless (first match wins on read).
+fn organ_cache(j: String, marker: String, mlen: Int, win_len: Int, need: String) -> [String] {
+    let m: [String] = native_list_empty()
+    let jl: Int = str_len(j)
+    let off: Int = 0
+    while off < jl {
+        let rest: String = str_slice(j, off, jl)
+        let p: Int = str_index_of(rest, marker)
+        if p < 0 {
+            off = jl
+        } else {
+            let abs: Int = off + p
+            let win: String = str_slice(j, abs, abs + win_len)
+            let after: String = str_slice(win, mlen, str_len(win))
+            let sp: Int = str_index_of(after, " ")
+            let hasneed: Int = str_index_of(win, need)
+            if sp > 0 {
+                if hasneed >= 0 {
+                    let sym: String = str_slice(after, 0, sp)
+                    m = native_list_append(m, sym)
+                    m = native_list_append(m, win)
+                }
+            }
+            off = abs + mlen
+        }
+    }
+    return m
+}
+
+// Load the phonetics organ file and cache sym -> content. mlen("phoneme ")=8.
+fn organ_pmap(path: String) -> [String] {
+    let ok: Bool = engram_load(path)
+    if ok == false {
+        return native_list_empty()
+    }
+    let j: String = engram_scan_nodes_json(600, 0)
+    return organ_cache(j, "phoneme ", 8, 150, "f1=")
+}
+
+// Load the accent organ file and cache sym -> content. mlen("accent_target ")=14.
+// Vowel overrides carry f1=..; the R rule carries drop_coda_r (need="=" matches
+// both, i.e. any well-formed accent_target field).
+fn organ_amap(path: String) -> [String] {
+    let ok: Bool = engram_load(path)
+    if ok == false {
+        return native_list_empty()
+    }
+    let j: String = engram_scan_nodes_json(600, 0)
+    return organ_cache(j, "accent_target ", 14, 90, "=")
+}
+
+// Vowel-set (categorical class) from the phonetics .psv class column.
+fn organ_vset(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let v: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 5 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 12 {
+                if str_eq(native_list_get(f, 11), "vowel") {
+                    v = native_list_append(v, native_list_get(f, 0))
+                }
+            }
+        }
+        li = li + 1
+    }
+    return v
+}
+
+// Word -> phoneme-sequence cache from lexicon.psv (engram-independent).
+fn organ_lex(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let m: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 3 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 2 {
+                m = native_list_append(m, native_list_get(f, 0))
+                m = native_list_append(m, native_list_get(f, 1))
+            }
+        }
+        li = li + 1
+    }
+    return m
+}
diff --git a/elp/src/speech-ingest.el b/elp/src/speech-ingest.el
new file mode 100644
index 0000000..796443d
--- /dev/null
+++ b/elp/src/speech-ingest.el
@@ -0,0 +1,233 @@
+// speech-ingest.el - The native LOAD step of the ingest organ, for the SPEECH
+// primitives. Reads the acoustic-phonetics SOURCE (elp/data/phonetics.psv) and
+// the pronunciation lexicon SOURCE (elp/data/lexicon.psv) and emits a PHONEME
+// MANIFOLD into the engram: one node per phoneme (faithful, provenance-tagged
+// content) + is_a edges to phoneme-class nodes (a discrete manifold, not islands).
+// The render then PULLS phoneme geometry back from the engram via phon_geo —
+// zero phonetic numbers in code. Source -> manifold -> merge; the same output
+// the polymorphic ingest organ will produce and subsume.
+
+// -- small parsing helpers ---------------------------------------------------
+fn sp_map_get(pairs: [String], key: String) -> String {
+    let n: Int = native_list_len(pairs)
+    let i: Int = 0
+    while i < n - 1 {
+        let k: String = native_list_get(pairs, i)
+        if str_eq(k, key) {
+            return native_list_get(pairs, i + 1)
+        }
+        let i = i + 2
+    }
+    return ""
+}
+
+// read the unsigned integer that follows `key` inside string s (e.g. key "F1=")
+fn parse_uint_from(s: String, key: String) -> Int {
+    let idx: Int = str_index_of(s, key)
+    if idx < 0 {
+        return 0
+    }
+    let start: Int = idx + str_len(key)
+    let n: Int = str_len(s)
+    let i: Int = start
+    let val: Int = 0
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        if c >= 48 {
+            if c <= 57 {
+                val = val * 10 + (c - 48)
+                i = i + 1
+            } else {
+                i = n
+            }
+        } else {
+            i = n
+        }
+    }
+    return val
+}
+
+fn clean_word(w: String) -> String {
+    let low: String = str_to_lower(w)
+    let n: Int = str_len(low)
+    let out: String = ""
+    let i: Int = 0
+    while i < n {
+        let c: Int = str_char_code(low, i)
+        if c >= 97 {
+            if c <= 122 {
+                out = out + str_char_at(low, i)
+            }
+        }
+        i = i + 1
+    }
+    return out
+}
+
+// -- INGEST: acoustic-phonetics source -> phoneme manifold in the engram ------
+// Returns the symbol -> node-id index (pmap) the render reads geometry through.
+fn ingest_phonetics(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let pmap: [String] = native_list_empty()
+    let classmap: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 5 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 12 {
+                let sym: String = native_list_get(f, 0)
+                let f1: String = native_list_get(f, 1)
+                let f2: String = native_list_get(f, 2)
+                let f3: String = native_list_get(f, 3)
+                let b1: String = native_list_get(f, 4)
+                let b2: String = native_list_get(f, 5)
+                let b3: String = native_list_get(f, 6)
+                let vo: String = native_list_get(f, 7)
+                let na: String = native_list_get(f, 8)
+                let du: String = native_list_get(f, 9)
+                let am: String = native_list_get(f, 10)
+                let cls: String = native_list_get(f, 11)
+                let cont: String = "phoneme " + sym + " | f1=" + f1 + " f2=" + f2 + " f3=" + f3 + " bw1=" + b1 + " bw2=" + b2 + " bw3=" + b3 + " voiced=" + vo + " nasal=" + na + " dur=" + du + " amp=" + am + " class=" + cls + " src=PetersonBarney1952-Hillenbrand1995"
+                let id: String = engram_node(cont, "Phoneme", 80)
+                pmap = native_list_append(pmap, sym)
+                pmap = native_list_append(pmap, cont)
+                // manifold edge: phoneme is_a class
+                let cid: String = sp_map_get(classmap, cls)
+                if str_eq(cid, "") {
+                    cid = engram_node("phoneme-class " + cls + " src=acoustic-phonetics", "PhonemeClass", 80)
+                    classmap = native_list_append(classmap, cls)
+                    classmap = native_list_append(classmap, cid)
+                }
+                engram_connect(id, cid, 80, "is_a")
+            }
+        }
+        li = li + 1
+    }
+    return pmap
+}
+
+// -- INGEST: pronunciation lexicon source -> word nodes ----------------------
+fn ingest_lexicon(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let lmap: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 3 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 2 {
+                let word: String = native_list_get(f, 0)
+                let seq: String = native_list_get(f, 1)
+                let id: String = engram_node("word " + word + " phonemes " + seq + " src=lexicon", "Pronunciation", 80)
+                lmap = native_list_append(lmap, word)
+                lmap = native_list_append(lmap, seq)
+            }
+        }
+        li = li + 1
+    }
+    return lmap
+}
+
+// -- READ geometry back from the engram (the render's afferent lookup) --------
+// phon_geo(sym) -> [F1,F2,F3,B1,B2,B3,voiced,nasal,dur,amp], parsed from the
+// ingested phoneme node's content. NO formant numbers live in this code.
+fn phon_geo(pmap: [String], sym: String) -> [Int] {
+    let id: String = sp_map_get(pmap, sym)
+    if str_eq(id, "") {
+        id = sp_map_get(pmap, "AX")
+    }
+    let out: [Int] = native_list_empty()
+    if str_eq(id, "") {
+        let out = native_list_append(out, 500)
+        let out = native_list_append(out, 1500)
+        let out = native_list_append(out, 2500)
+        let out = native_list_append(out, 80)
+        let out = native_list_append(out, 100)
+        let out = native_list_append(out, 150)
+        let out = native_list_append(out, 1)
+        let out = native_list_append(out, 0)
+        let out = native_list_append(out, 80)
+        let out = native_list_append(out, 80)
+        return out
+    }
+    let j: String = id
+    let out = native_list_append(out, parse_uint_from(j, "f1="))
+    let out = native_list_append(out, parse_uint_from(j, "f2="))
+    let out = native_list_append(out, parse_uint_from(j, "f3="))
+    let out = native_list_append(out, parse_uint_from(j, "bw1="))
+    let out = native_list_append(out, parse_uint_from(j, "bw2="))
+    let out = native_list_append(out, parse_uint_from(j, "bw3="))
+    let out = native_list_append(out, parse_uint_from(j, "voiced="))
+    let out = native_list_append(out, parse_uint_from(j, "nasal="))
+    let out = native_list_append(out, parse_uint_from(j, "dur="))
+    let out = native_list_append(out, parse_uint_from(j, "amp="))
+    return out
+}
+
+// word -> phoneme codes, read from the ingested lexicon node.
+fn word_phonemes(lmap: [String], word: String) -> [String] {
+    let id: String = sp_map_get(lmap, word)
+    if str_eq(id, "") {
+        let r: [String] = native_list_empty()
+        let r = native_list_append(r, "AX")
+        return r
+    }
+    return str_split(id, " ")
+}
+
+// realized text -> flat phoneme-code sequence (SIL between words + at ends).
+fn text_phonemes(lmap: [String], text: String) -> [String] {
+    let words: [String] = str_split(text, " ")
+    let nw: Int = native_list_len(words)
+    let seq: [String] = native_list_empty()
+    let seq = native_list_append(seq, "SIL")
+    let wi: Int = 0
+    while wi < nw {
+        let raw: String = native_list_get(words, wi)
+        let w: String = clean_word(raw)
+        if str_eq(w, "") {
+            wi = wi + 1
+        } else {
+            let ph: [String] = word_phonemes(lmap, w)
+            let np: Int = native_list_len(ph)
+            let pi: Int = 0
+            while pi < np {
+                let code: String = native_list_get(ph, pi)
+                seq = native_list_append(seq, code)
+                pi = pi + 1
+            }
+            seq = native_list_append(seq, "SIL")
+            wi = wi + 1
+        }
+    }
+    return seq
+}
diff --git a/elp/src/speech.el b/elp/src/speech.el
new file mode 100644
index 0000000..d6f04df
--- /dev/null
+++ b/elp/src/speech.el
@@ -0,0 +1,460 @@
+// speech.el - The native SPEECH render path + voice-by-imitation extractor.
+//
+// Speech = the AUDIO surface (surface_profile_audio) rendering LANGUAGE-meaning
+// through a VOICE signature. The realizer's language faculty supplies the words
+// (meaning -> sem_realize -> text); this module turns text -> phonemes (phonetics.el)
+// -> a formant-target track over time -> SUPERPOSES formant resonances over a
+// glottal source (own-core formant synthesis, the exact integer mirror of the
+// music additive superpose) -> own-core PCM/WAV. Two paths:
+//   (1) RENDER:  speak(text, voice) -> spoken WAV.
+//   (2) IMITATE: voice_analyze(pcm) -> a voice signature grabbed BY EAR
+//                (autocorrelation pitch + integer-DFT formant peaks), then render
+//                any new meaning in that voice. An impression, not a corpus.
+// All integer/fixed-point (EL float arithmetic is unusable).
+
+// -- Own-core integer sine (Bhaskara I), phase 0..65535 = one cycle -----------
+fn sp_sin(phase: Int) -> Int {
+    let deg: Int = phase * 360 / 65536
+    let neg: Int = 0
+    if deg > 180 {
+        deg = deg - 180
+        neg = 1
+    }
+    let t: Int = deg * (180 - deg)
+    let num: Int = 32767 * 4 * t
+    let den: Int = 40500 - t
+    let v: Int = num / den
+    if neg == 1 {
+        v = 0 - v
+    }
+    return v
+}
+
+fn sp_cos(phase: Int) -> Int {
+    let p: Int = phase + 16384
+    p = p - (p / 65536) * 65536
+    return sp_sin(p)
+}
+
+// One formant resonance (Lorentzian peak), Q15. Peak 32767 at f=fc.
+fn sp_gain(f: Int, fc: Int, bw: Int) -> Int {
+    let d: Int = f - fc
+    let den: Int = d * d + bw * bw
+    let num: Int = 32767 * bw * bw
+    return num / den
+}
+
+fn sp_isqrt(n: Int) -> Int {
+    if n <= 0 {
+        return 0
+    }
+    let x: Int = n
+    let y: Int = (x + 1) / 2
+    while y < x {
+        x = y
+        y = (x + n / x) / 2
+    }
+    return x
+}
+
+// -- WAV serializer (thin medium; the only non-DSP glue) ---------------------
+fn wav_le16(buf: String, off: Int, v: Int) -> String {
+    let u: Int = v
+    if u < 0 {
+        u = u + 65536
+    }
+    let lo: Int = u - (u / 256) * 256
+    let hi: Int = u / 256
+    let b: String = __str_set_char(buf, off, lo)
+    b = __str_set_char(b, off + 1, hi)
+    return b
+}
+
+fn wav_le32(buf: String, off: Int, v: Int) -> String {
+    let b0: Int = v - (v / 256) * 256
+    let r1: Int = v / 256
+    let b1: Int = r1 - (r1 / 256) * 256
+    let r2: Int = r1 / 256
+    let b2: Int = r2 - (r2 / 256) * 256
+    let b3: Int = r2 / 256
+    let b: String = __str_set_char(buf, off, b0)
+    b = __str_set_char(b, off + 1, b1)
+    b = __str_set_char(b, off + 2, b2)
+    b = __str_set_char(b, off + 3, b3)
+    return b
+}
+
+fn wav_ascii(buf: String, off: Int, s: String) -> String {
+    let n: Int = str_len(s)
+    let i: Int = 0
+    let b: String = buf
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        b = __str_set_char(b, off + i, c)
+        i = i + 1
+    }
+    return b
+}
+
+fn write_wav(samples: [Int], sr: Int, path: String) -> Bool {
+    let ns: Int = native_list_len(samples)
+    let datalen: Int = ns * 2
+    let total: Int = 44 + datalen
+    let buf: String = __str_alloc(total)
+    buf = wav_ascii(buf, 0, "RIFF")
+    buf = wav_le32(buf, 4, 36 + datalen)
+    buf = wav_ascii(buf, 8, "WAVE")
+    buf = wav_ascii(buf, 12, "fmt ")
+    buf = wav_le32(buf, 16, 16)
+    buf = wav_le16(buf, 20, 1)
+    buf = wav_le16(buf, 22, 1)
+    buf = wav_le32(buf, 24, sr)
+    buf = wav_le32(buf, 28, sr * 2)
+    buf = wav_le16(buf, 32, 2)
+    buf = wav_le16(buf, 34, 16)
+    buf = wav_ascii(buf, 36, "data")
+    buf = wav_le32(buf, 40, datalen)
+    let j: Int = 0
+    let off: Int = 44
+    while j < ns {
+        let raw: Int = native_list_get(samples, j)
+        buf = wav_le16(buf, off, raw)
+        off = off + 2
+        j = j + 1
+    }
+    return __fs_write_bytes(path, buf, total)
+}
+
+// One formant resonance as a float Lorentzian peak (own-core physics).
+fn fgain(f: Float, fc: Float, bw: Float) -> Float {
+    let d: Float = f - fc
+    return (bw * bw) / (d * d + bw * bw)
+}
+
+// His PITCH MELODY from measured prosody [f0_median, f0_min, f0_max, declination].
+// A natural statement shape over the utterance: onset rise to the median, a
+// near-flat body (his declination is ~0.6 Hz/s), and a final fall toward f0_min.
+// Follows his melody + range, not a fixed 0.85 decline. gidx/total = position.
+fn prosody_f0(pros: [Int], gidx: Int, total: Int) -> Int {
+    let med: Int = native_list_get(pros, 0)
+    let lo: Int = native_list_get(pros, 1)
+    let hi: Int = native_list_get(pros, 2)
+    let p: Int = gidx * 1000 / total
+    let f0: Int = med
+    if p < 150 {
+        f0 = lo + (med - lo) * p / 150
+    } else {
+        if p > 700 {
+            f0 = med + (lo - med) * (p - 700) / 300
+        } else {
+            f0 = med
+        }
+    }
+    if f0 < lo {
+        f0 = lo
+    }
+    if f0 > hi {
+        f0 = hi
+    }
+    return f0
+}
+
+// -- The render: phoneme codes + voice signature -> normalized PCM samples ----
+// Formant geometry per phoneme is READ FROM THE ENGRAM (pmap) via phon_geo — no
+// table in code. The optional ACCENT map (amap) composes a transform onto the
+// voice (voice (+) accent, separable): RP formant overrides read from the accent
+// manifold + a non-rhotic coda-R drop. Empty amap = base General-American.
+// Synthesis is FLOAT: a real phase accumulator + math_sin, superposition physics.
+fn synth_codes_accent(codes0: [String], voice: [String], pmap: [String], amap: [String], vset: [String], vmap: [String], prosody: [Int]) -> [Int] {
+    let sr: Int = 16000
+    let srf: Float = 16000.0
+    let two_pi: Float = 6.283185307
+    let kf: Int = voice_get_int(voice, "kf")
+    let f0s: Int = voice_get_int(voice, "f0")
+    let f0e: Int = voice_get_int(voice, "f0_end")
+    let durm: Int = voice_get_int(voice, "dur")
+    if kf <= 0 {
+        kf = 1000
+    }
+    if durm <= 0 {
+        durm = 1000
+    }
+    let use_accent: Int = 0
+    if native_list_len(amap) > 0 {
+        use_accent = 1
+    }
+    let codes: [String] = codes0
+    if use_accent == 1 {
+        if is_nonrhotic(amap) == 1 {
+            codes = apply_rhoticity(codes0, vset)
+        }
+    }
+    let nc: Int = native_list_len(codes)
+
+    // pass 1: per-segment sample counts + total
+    let segn: [Int] = native_list_empty()
+    let total: Int = 0
+    let ci: Int = 0
+    while ci < nc {
+        let code: String = native_list_get(codes, ci)
+        let p: [Int] = phon_geo(pmap, code)
+        let durms: Int = native_list_get(p, 8)
+        let ns: Int = durms * 16 * durm / 1000
+        segn = native_list_append(segn, ns)
+        total = total + ns
+        ci = ci + 1
+    }
+    if total <= 0 {
+        total = 1
+    }
+
+    // pass 2: synthesize
+    let samples: [Int] = native_list_empty()
+    let phasef: Float = 0.0
+    let gidx: Int = 0
+    let prevF1: Int = 500 * kf / 1000
+    let prevF2: Int = 1500 * kf / 1000
+    let prevF3: Int = 2500 * kf / 1000
+    let nstate: Int = 22695
+    let maxabs: Int = 1
+
+    let ci2: Int = 0
+    while ci2 < nc {
+        let code: String = native_list_get(codes, ci2)
+        let p: [Int] = phon_geo(pmap, code)
+        let rf1: Int = native_list_get(p, 0)
+        let rf2: Int = native_list_get(p, 1)
+        let rf3: Int = native_list_get(p, 2)
+        if use_accent == 1 {
+            let ov: [Int] = accent_formants(amap, code)
+            if native_list_len(ov) >= 3 {
+                rf1 = native_list_get(ov, 0)
+                rf2 = native_list_get(ov, 1)
+                rf3 = native_list_get(ov, 2)
+            }
+        }
+        // HIS measured vowel target overrides the generic/kf path (absolute Hz —
+        // his formants already encode his vocal tract, so no kf scaling).
+        let usekf: Int = 1
+        if native_list_len(vmap) > 0 {
+            let hv: [Int] = vmap_get(vmap, code)
+            if native_list_len(hv) >= 3 {
+                rf1 = native_list_get(hv, 0)
+                rf2 = native_list_get(hv, 1)
+                rf3 = native_list_get(hv, 2)
+                usekf = 0
+            }
+        }
+        let F1t: Int = rf1 * kf / 1000
+        let F2t: Int = rf2 * kf / 1000
+        let F3t: Int = rf3 * kf / 1000
+        if usekf == 0 {
+            F1t = rf1
+            F2t = rf2
+            F3t = rf3
+        }
+        let B1: Int = native_list_get(p, 3)
+        let B2: Int = native_list_get(p, 4)
+        let B3: Int = native_list_get(p, 5)
+        let voiced: Int = native_list_get(p, 6)
+        let ampv: Int = native_list_get(p, 9)
+        let ns: Int = native_list_get(segn, ci2)
+        let trans: Int = ns / 2
+        if trans > 560 {
+            trans = 560
+        }
+        if trans < 1 {
+            trans = 1
+        }
+        let k: Int = 0
+        while k < ns {
+            let cF1: Int = F1t
+            let cF2: Int = F2t
+            let cF3: Int = F3t
+            if k < trans {
+                cF1 = prevF1 + (F1t - prevF1) * k / trans
+                cF2 = prevF2 + (F2t - prevF2) * k / trans
+                cF3 = prevF3 + (F3t - prevF3) * k / trans
+            }
+            let f0c: Int = f0s + (f0e - f0s) * gidx / total
+            if native_list_len(prosody) >= 3 {
+                f0c = prosody_f0(prosody, gidx, total)
+            }
+            if f0c < 40 {
+                f0c = 40
+            }
+            let env: Int = 32767
+            let ar: Int = 96
+            if k < ar {
+                env = 32767 * k / ar
+            }
+            let tail: Int = ns - k
+            if tail < ar {
+                env = 32767 * tail / ar
+            }
+            let f0cf: Float = int_to_float(f0c)
+            phasef = phasef + two_pi * f0cf / srf
+            if phasef > two_pi {
+                phasef = phasef - two_pi
+            }
+
+            let s: Int = 0
+            if voiced == 1 {
+                let cF1f: Float = int_to_float(cF1)
+                let cF2f: Float = int_to_float(cF2)
+                let cF3f: Float = int_to_float(cF3)
+                let B1f: Float = int_to_float(B1)
+                let B2f: Float = int_to_float(B2)
+                let B3f: Float = int_to_float(B3)
+                let acc: Float = 0.0
+                let h: Int = 1
+                while h <= 50 {
+                    let hf: Float = int_to_float(h)
+                    let fhf: Float = hf * f0cf
+                    if fhf < 7900.0 {
+                        let sv: Float = math_sin(phasef * hf)
+                        let src: Float = 1.0 / hf
+                        let g1: Float = fgain(fhf, cF1f, B1f)
+                        let g2: Float = fgain(fhf, cF2f, B2f)
+                        let g3: Float = fgain(fhf, cF3f, B3f)
+                        let g: Float = g1 + g2 + g3
+                        acc = acc + src * g * sv
+                    }
+                    h = h + 1
+                }
+                s = float_to_int(acc * 4000.0)
+            } else {
+                if ampv > 0 {
+                    nstate = nstate * 1103515245 + 12345
+                    nstate = nstate - (nstate / 2147483648) * 2147483648
+                    if nstate < 0 {
+                        nstate = 0 - nstate
+                    }
+                    let nz: Int = nstate / 32768 - 32768
+                    s = nz
+                }
+            }
+            s = s * ampv / 100
+            s = s * env / 32767
+            samples = native_list_append(samples, s)
+            let a: Int = s
+            if a < 0 {
+                a = 0 - a
+            }
+            if a > maxabs {
+                maxabs = a
+            }
+            gidx = gidx + 1
+            k = k + 1
+        }
+        prevF1 = F1t
+        prevF2 = F2t
+        prevF3 = F3t
+        ci2 = ci2 + 1
+    }
+
+    // normalize to int16 range (~22000 peak)
+    let out: [Int] = native_list_empty()
+    let ntot: Int = native_list_len(samples)
+    let j: Int = 0
+    while j < ntot {
+        let raw: Int = native_list_get(samples, j)
+        let v: Int = raw * 22000 / maxabs
+        out = native_list_append(out, v)
+        j = j + 1
+    }
+    return out
+}
+
+// GA convenience wrapper (no accent) — keeps the base render path.
+fn synth_codes(codes: [String], voice: [String], pmap: [String]) -> [Int] {
+    let noacc: [String] = native_list_empty()
+    let novset: [String] = native_list_empty()
+    let novmap: [String] = native_list_empty()
+    let nopros: [Int] = native_list_empty()
+    return synth_codes_accent(codes, voice, pmap, noacc, novset, novmap, nopros)
+}
+
+// -- Voice-by-imitation: HEAR a PCM sample -> extract the voice signature -----
+// Pitch by autocorrelation; vocal-tract scale (kf) from the F1 formant peak of a
+// heard sustained vowel /AA/ (nominal F1 = 730 Hz) via an integer DFT. The
+// analyzer sees ONLY the PCM samples — never the source signature numbers — so
+// recovery is genuinely by ear.
+fn voice_f0(samples: [Int], sr: Int) -> Int {
+    let n: Int = native_list_len(samples)
+    let start: Int = n / 4
+    let end: Int = n * 3 / 4
+    // bound the analysis window so accumulators can never overflow on long input
+    if end - start > 6000 {
+        end = start + 6000
+    }
+    let minlag: Int = sr / 300
+    let maxlag: Int = sr / 75
+    let best: Int = 0
+    let bestlag: Int = minlag
+    let lag: Int = minlag
+    while lag <= maxlag {
+        let sum: Int = 0
+        let i: Int = start
+        while i < end {
+            let ai: Int = native_list_get(samples, i)
+            let bi: Int = native_list_get(samples, i + lag)
+            sum = sum + ai * bi / 256
+            i = i + 2
+        }
+        if sum > best {
+            best = sum
+            bestlag = lag
+        }
+        lag = lag + 1
+    }
+    if bestlag < 1 {
+        bestlag = 1
+    }
+    return sr / bestlag
+}
+
+fn voice_peak_in_band(samples: [Int], sr: Int, flo: Int, fhi: Int) -> Int {
+    let n: Int = native_list_len(samples)
+    let start: Int = n / 4
+    let end: Int = n * 3 / 4
+    // bound the DFT window: re/im are accumulated /4096, and re*re must stay in
+    // int64 — cap terms so (window/2)*(peak_term) squared cannot overflow.
+    if end - start > 3000 {
+        end = start + 3000
+    }
+    let bestmag: Int = 0
+    let bestf: Int = flo
+    let f: Int = flo
+    while f <= fhi {
+        let re: Int = 0
+        let im: Int = 0
+        let i: Int = start
+        while i < end {
+            let x: Int = native_list_get(samples, i)
+            let ph: Int = i * f * 65536 / sr
+            ph = ph - (ph / 65536) * 65536
+            let cq: Int = sp_cos(ph)
+            let sq: Int = sp_sin(ph)
+            re = re + x * cq / 4096
+            im = im + x * sq / 4096
+            i = i + 2
+        }
+        let mag: Int = re * re + im * im
+        if mag > bestmag {
+            bestmag = mag
+            bestf = f
+        }
+        f = f + 25
+    }
+    return bestf
+}
+
+// Analyze a heard sustained /AA/ -> a full voice signature (by ear).
+fn voice_analyze(samples: [Int], sr: Int) -> [String] {
+    let f0: Int = voice_f0(samples, sr)
+    let f1: Int = voice_peak_in_band(samples, sr, 450, 1150)
+    let kf: Int = 1000 * f1 / 730
+    let f0e: Int = f0 * 85 / 100
+    return voice_new("imitated", f0, f0e, kf, 1000, 1000, 8)
+}
diff --git a/elp/src/voice-ingest.el b/elp/src/voice-ingest.el
new file mode 100644
index 0000000..e36647f
--- /dev/null
+++ b/elp/src/voice-ingest.el
@@ -0,0 +1,244 @@
+// voice-ingest.el - The LIVE VOICE LOOP reshape + ingest-as-geometry.
+//
+// EL cannot read a binary WAV (fs_read NUL-truncates), so the thin-medium DSP
+// extractor is periph's `voiceprint` (autocorr F0 + LPC formants), equivalent to
+// our own voice_analyze. This module: (1) RESHAPE the voiceprint JSON (TEXT) into
+// the organ voice-signature schema; (2) INGEST it as a GEOMETRY manifold in the
+// engram and engram_save it to a file; (3) READ the target signature BACK from
+// that geometry (engram_load + scan + filter), never from the json or a table.
+// HONEST: this reaches for pitch + a coarse vocal-tract scale (kf). It is NOT a
+// clone — no glottal timbre, vowel-space, or articulation is captured.
+
+fn parse_leading_int(s: String) -> Int {
+    let n: Int = str_len(s)
+    let i: Int = 0
+    let v: Int = 0
+    let started: Int = 0
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        if c >= 48 {
+            if c <= 57 {
+                v = v * 10 + (c - 48)
+                started = 1
+                i = i + 1
+            } else {
+                i = n
+            }
+        } else {
+            if started == 1 {
+                i = n
+            } else {
+                i = i + 1
+            }
+        }
+    }
+    return v
+}
+
+// voiceprint JSON -> organ voice-signature source file; returns [f0,f0_end,kf,f1,f2,f3].
+fn reshape_voiceprint(vppath: String, outjson: String) -> [Int] {
+    let j: String = fs_read(vppath)
+    let f0: Int = parse_uint_from(j, "f0_hz\":")
+    let fp: Int = str_index_of(j, "formants_hz")
+    let tail: String = str_slice(j, fp, fp + 120)
+    let br: Int = str_index_of(tail, "[")
+    let arr: String = str_slice(tail, br + 1, str_len(tail))
+    let f1: Int = parse_leading_int(arr)
+    let c1: Int = str_index_of(arr, ",")
+    let a2: String = str_slice(arr, c1 + 1, str_len(arr))
+    let f2: Int = parse_leading_int(a2)
+    let c2: Int = str_index_of(a2, ",")
+    let a3: String = str_slice(a2, c2 + 1, str_len(a2))
+    let f3: Int = parse_leading_int(a3)
+    let f0e: Int = f0 * 85 / 100
+    // derive kf honestly: coarse vocal-tract scale from the formant pattern
+    let t1: Int = 1000 * f1 / 500
+    let t2: Int = 1000 * f2 / 1500
+    let t3: Int = 1000 * f3 / 2500
+    let kf: Int = (t1 + t2 + t3) / 3
+    if kf < 800 {
+        kf = 800
+    }
+    if kf > 1400 {
+        kf = 1400
+    }
+    let js: String = "{\"dataset\":\"will-voice-signature\",\"primitive_type\":\"voice\",\"grounding\":\"measured\",\"provenance\":\"Will live 30s read 2026-08-15 (elp/data/live/will30_clean.wav, 27.0s) SUPERSEDES the coarse 10s sample; F0+formants via periph voiceprint (autocorr+LPC), averaged over his full vowel set. Still the 11-number average: no coarticulation/prosody. COARSE — pitch + vocal-tract scale, NOT a clone.\",\"records\":[{\"key\":\"will\",\"features\":{\"source\":\"live-mic\"},\"attributes\":{\"f0\":" + int_to_str(f0) + ",\"f0_end\":" + int_to_str(f0e) + ",\"kf\":" + int_to_str(kf) + ",\"f1\":" + int_to_str(f1) + ",\"f2\":" + int_to_str(f2) + ",\"f3\":" + int_to_str(f3) + "}}]}"
+    let okw: Bool = fs_write(outjson, js)
+    let r: [Int] = native_list_empty()
+    let r = native_list_append(r, f0)
+    let r = native_list_append(r, f0e)
+    let r = native_list_append(r, kf)
+    let r = native_list_append(r, f1)
+    let r = native_list_append(r, f2)
+    let r = native_list_append(r, f3)
+    return r
+}
+
+// Ingest the signature as a manifold (a set-hub + the will node + a member edge)
+// and engram_save it to a reloadable file. grounding:measured self-declared.
+fn ingest_voice(sig: [Int], savepath: String) -> Int {
+    let f0: Int = native_list_get(sig, 0)
+    let f0e: Int = native_list_get(sig, 1)
+    let kf: Int = native_list_get(sig, 2)
+    let f1: Int = native_list_get(sig, 3)
+    let f2: Int = native_list_get(sig, 4)
+    let f3: Int = native_list_get(sig, 5)
+    let hub: String = engram_node("voice-signature-set will grounding=measured src=periph-voiceprint", "VoiceSet", 90)
+    let cont: String = "voice will | f0=" + int_to_str(f0) + " f0_end=" + int_to_str(f0e) + " kf=" + int_to_str(kf) + " f1=" + int_to_str(f1) + " f2=" + int_to_str(f2) + " f3=" + int_to_str(f3) + " grounding=measured src=periph-voiceprint-30s supersedes=prior-voice-region prov=COARSE-pitch+tractscale-NOT-a-clone"
+    let id: String = engram_node(cont, "Voice", 90)
+    engram_connect(id, hub, 90, "member_of")
+    let oks: Bool = engram_save(savepath)
+    return 1
+}
+
+// READ the target voice back FROM the ingested geometry (engram_load + scan +
+// client-filter for "voice will"). Returns [f0,f0_end,kf,f1,f2,f3] or empty.
+fn load_voice(savepath: String) -> [Int] {
+    let ok: Bool = engram_load(savepath)
+    let r: [Int] = native_list_empty()
+    if ok == false {
+        return r
+    }
+    let j: String = engram_scan_nodes_json(200, 0)
+    let p: Int = str_index_of(j, "voice will ")
+    if p < 0 {
+        return r
+    }
+    let win: String = str_slice(j, p, p + 200)
+    let r = native_list_append(r, parse_uint_from(win, "f0="))
+    let r = native_list_append(r, parse_uint_from(win, "f0_end="))
+    let r = native_list_append(r, parse_uint_from(win, "kf="))
+    let r = native_list_append(r, parse_uint_from(win, "f1="))
+    let r = native_list_append(r, parse_uint_from(win, "f2="))
+    let r = native_list_append(r, parse_uint_from(win, "f3="))
+    return r
+}
+
+// ---- Vowel-space + prosody: ingest-as-geometry + read-back (no source layer) --
+// vowel target lookup from the ingested vowel-space manifold: sym -> [f1,f2,f3].
+fn vmap_get(vmap: [String], code: String) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(vmap, code)
+    if str_eq(id, "") {
+        return out
+    }
+    let f1: Int = parse_uint_from(id, "f1=")
+    if f1 <= 0 {
+        return out
+    }
+    let out = native_list_append(out, f1)
+    let out = native_list_append(out, parse_uint_from(id, "f2="))
+    let out = native_list_append(out, parse_uint_from(id, "f3="))
+    return out
+}
+
+// Ingest his measured vowel space + prosody as ONE manifold (VowelSpace hub +
+// per-vowel target nodes + a prosody node) and engram_save it. Fresh empty store
+// per run => set-replace, no duplicate.
+fn ingest_voicegeom(vpath: String, ppath: String, savepath: String) -> Int {
+    let hub: String = engram_node("vowel-space-set will grounding=measured src=lpc-formant-track-30s", "VowelSpace", 90)
+    let content: String = fs_read(vpath)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 5 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 5 {
+                let sym: String = native_list_get(f, 0)
+                let cont: String = "vowel-target will " + sym + " | f1=" + native_list_get(f, 1) + " f2=" + native_list_get(f, 2) + " f3=" + native_list_get(f, 3) + " n=" + native_list_get(f, 4) + " grounding=measured src=lpc-formant-track-30s"
+                let id: String = engram_node(cont, "VowelTarget", 90)
+                engram_connect(id, hub, 90, "member_of")
+            }
+        }
+        li = li + 1
+    }
+    let pc: String = fs_read(ppath)
+    let plines: [String] = str_split(pc, "\n")
+    let pnl: Int = native_list_len(plines)
+    let pi: Int = 0
+    while pi < pnl {
+        let pl: String = native_list_get(plines, pi)
+        let ok2: Int = 1
+        if str_len(pl) < 5 {
+            ok2 = 0
+        }
+        if ok2 == 1 {
+            if str_char_code(pl, 0) == 35 {
+                ok2 = 0
+            }
+        }
+        if ok2 == 1 {
+            let pf: [String] = str_split(pl, "|")
+            if native_list_len(pf) >= 4 {
+                let pcont: String = "prosody will | f0_median=" + native_list_get(pf, 0) + " f0_min=" + native_list_get(pf, 1) + " f0_max=" + native_list_get(pf, 2) + " declination=" + native_list_get(pf, 3) + " src=f0-contour-30s"
+                let pid: String = engram_node(pcont, "Prosody", 90)
+                engram_connect(pid, hub, 90, "prosody_of")
+            }
+        }
+        pi = pi + 1
+    }
+    let oks: Bool = engram_save(savepath)
+    return 1
+}
+
+// Read the vowel-space back from geometry; prosody folded under key __PROSODY__.
+fn load_voicegeom(savepath: String) -> [String] {
+    let m: [String] = native_list_empty()
+    let ok: Bool = engram_load(savepath)
+    if ok == false {
+        return m
+    }
+    let j: String = engram_scan_nodes_json(400, 0)
+    let jl: Int = str_len(j)
+    let off: Int = 0
+    while off < jl {
+        let rest: String = str_slice(j, off, jl)
+        let p: Int = str_index_of(rest, "vowel-target will ")
+        if p < 0 {
+            off = jl
+        } else {
+            let abs: Int = off + p
+            let win: String = str_slice(j, abs, abs + 140)
+            let after: String = str_slice(win, 18, str_len(win))
+            let sp: Int = str_index_of(after, " ")
+            if sp > 0 {
+                let sym: String = str_slice(after, 0, sp)
+                m = native_list_append(m, sym)
+                m = native_list_append(m, win)
+            }
+            off = abs + 18
+        }
+    }
+    let pp: Int = str_index_of(j, "prosody will ")
+    if pp >= 0 {
+        let pwin: String = str_slice(j, pp, pp + 160)
+        m = native_list_append(m, "__PROSODY__")
+        m = native_list_append(m, pwin)
+    }
+    return m
+}
+
+// Prosody stats [f0_median, f0_min, f0_max, declination] read from geometry.
+fn prosody_from(vmap: [String]) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(vmap, "__PROSODY__")
+    if str_eq(id, "") {
+        return out
+    }
+    let out = native_list_append(out, parse_uint_from(id, "f0_median="))
+    let out = native_list_append(out, parse_uint_from(id, "f0_min="))
+    let out = native_list_append(out, parse_uint_from(id, "f0_max="))
+    let out = native_list_append(out, parse_uint_from(id, "declination="))
+    return out
+}
diff --git a/elp/src/voice-profile.el b/elp/src/voice-profile.el
new file mode 100644
index 0000000..854f173
--- /dev/null
+++ b/elp/src/voice-profile.el
@@ -0,0 +1,85 @@
+// voice-profile.el - The VOICE signature as a pluggable PROFILE.
+//
+// Exact mirror of surface-profile.el / language-profile.el: a voice is a
+// [String] slot-map read via voice_get, the SAME mechanism the realizer uses
+// for language and surface. Where an instrument signature (a few dozen numbers)
+// is the timbre of a musical tone, a VOICE signature is the timbre of the vocal
+// tract — the instrument that renders LANGUAGE-meaning as SPEECH on the audio
+// surface. Physics (source-filter), not a recorded corpus.
+//
+// The signature is a few numbers, all integer (EL float arithmetic is unusable):
+//   name    - label
+//   f0      - base pitch, Hz (glottal source rate at utterance start)
+//   f0_end  - pitch at utterance end (declination -> falling = declarative)
+//   kf      - formant scale in PER-MILLE (1000 = x1.0). Encodes vocal-tract
+//             length: shorter tract (child/female) -> higher kf. Scales every
+//             phoneme's nominal formant: F_actual = F_nominal * kf / 1000.
+//   dur     - speaking-rate multiplier in per-mille (1000 = nominal; >1000 slower)
+//   tilt    - source spectral tilt (per-mille; higher = darker/steeper rolloff)
+//   breath  - breathiness 0..100 (aspiration mixed into the source)
+//
+// A voice is grabbed BY EAR (voice_analyze in speech.el extracts these numbers
+// from a short PCM sample — an impression, not 10h of training), or declared.
+
+fn voice_new(name: String, f0: Int, f0_end: Int, kf: Int, dur: Int, tilt: Int, breath: Int) -> [String] {
+    let r: [String] = native_list_empty()
+    let r = native_list_append(r, "name")
+    let r = native_list_append(r, name)
+    let r = native_list_append(r, "f0")
+    let r = native_list_append(r, int_to_str(f0))
+    let r = native_list_append(r, "f0_end")
+    let r = native_list_append(r, int_to_str(f0_end))
+    let r = native_list_append(r, "kf")
+    let r = native_list_append(r, int_to_str(kf))
+    let r = native_list_append(r, "dur")
+    let r = native_list_append(r, int_to_str(dur))
+    let r = native_list_append(r, "tilt")
+    let r = native_list_append(r, int_to_str(tilt))
+    let r = native_list_append(r, "breath")
+    let r = native_list_append(r, int_to_str(breath))
+    return r
+}
+
+// Accessor — identical convention to surface_get / lang_get.
+fn voice_get(profile: [String], key: String) -> String {
+    let n: Int = native_list_len(profile)
+    let i: Int = 0
+    while i < n - 1 {
+        let k: String = native_list_get(profile, i)
+        if str_eq(k, key) {
+            return native_list_get(profile, i + 1)
+        }
+        let i = i + 2
+    }
+    return ""
+}
+
+fn voice_get_int(profile: [String], key: String) -> Int {
+    let s: String = voice_get(profile, key)
+    if str_eq(s, "") {
+        return 0
+    }
+    return str_to_int(s)
+}
+
+// -- Built-in voices ---------------------------------------------------------
+
+// Neuron's own voice: calm, precise, androgynous-neutral. Low-ish base pitch,
+// gentle declination, near-neutral vocal-tract length.
+fn voice_neuron() -> [String] {
+    return voice_new("neuron", 112, 96, 1020, 1000, 1000, 6)
+}
+
+// Will's voice signature, built from the INGESTED geometry (f0/f0_end/kf read
+// back from the will-voice manifold — passed in, never hardcoded). Composable
+// with an accent transform exactly like voice_neuron() (voice (+) accent).
+fn voice_will(f0: Int, f0_end: Int, kf: Int) -> [String] {
+    return voice_new("will", f0, f0_end, kf, 1000, 1000, 6)
+}
+
+// A deliberately DISTINCT target voice for the imitation proof: higher pitch,
+// shorter vocal tract (kf=1.20) -> a clearly different speaker. Neuron will
+// HEAR a sample of this voice and reconstruct these numbers by ear.
+fn voice_target_a() -> [String] {
+    return voice_new("target_a", 178, 150, 1200, 950, 1000, 10)
+}
diff --git a/elp/tests/examples/speech-accent-demo.el b/elp/tests/examples/speech-accent-demo.el
new file mode 100644
index 0000000..17b69bd
--- /dev/null
+++ b/elp/tests/examples/speech-accent-demo.el
@@ -0,0 +1,45 @@
+// speech-accent-demo.el - PROOF: Neuron speaks with a BRITISH accent, where the
+// accent is a TRANSFORM composed onto the voice (voice (+) accent, separable),
+// INGESTED as geometry (not a table). Same voice, accent toggled on/off = RP/GA.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // LEARN: base phonetics + lexicon + the British-RP accent transform, all as
+    // ingested geometry (source -> manifold -> engram).
+    let pmap: [String] = ingest_phonetics("elp/data/phonetics.psv")
+    let lmap: [String] = ingest_lexicon("elp/data/lexicon.psv")
+    let amap: [String] = ingest_accent("elp/data/british-accent.psv")
+    println("[learn] phonemes=" + int_to_str(native_list_len(pmap) / 2) + " words=" + int_to_str(native_list_len(lmap) / 2) + " accent_targets=" + int_to_str(native_list_len(amap) / 2))
+
+    let neuron: [String] = voice_neuron()
+    let noaccent: [String] = native_list_empty()
+
+    // -- Sentence 1: "I am Neuron." from meaning ----------------------------
+    let fr1: [String] = sem_frame("describe", "I", "Neuron", "")
+    let t1: String = sem_realize(fr1)
+    let c1: [String] = text_phonemes(lmap, t1)
+    println("[s1] " + t1 + " :: " + list_join(c1, " "))
+
+    // separability: SAME voice, accent OFF (GA) vs ON (RP)
+    let ga: [Int] = synth_codes_accent(c1, neuron, pmap, noaccent)
+    let okga: Bool = write_wav(ga, 16000, outdir + "ga-neuron.wav")
+    let br1: [Int] = synth_codes_accent(c1, neuron, pmap, amap)
+    let okb1: Bool = write_wav(br1, 16000, outdir + "british-neuron.wav")
+
+    // -- Sentence 2: showcases NON-RHOTICITY --------------------------------
+    let fr2: [String] = sem_frame("describe", "I", "here", "")
+    let t2: String = sem_realize(fr2)
+    let c2: [String] = text_phonemes(lmap, t2)
+    let c2rp: [String] = apply_rhoticity(c2, pmap)
+    println("[s2] " + t2 + " :: GA=" + list_join(c2, " ") + "  RP=" + list_join(c2rp, " "))
+    let br2: [Int] = synth_codes_accent(c2, neuron, pmap, amap)
+    let okb2: Bool = write_wav(br2, 16000, outdir + "british-2.wav")
+
+    // show an RP override read straight from the accent geometry
+    let ovAA: [Int] = accent_formants(amap, "AA")
+    if native_list_len(ovAA) >= 3 {
+        println("[accent-geometry] AA(LOT) RP f1=" + int_to_str(native_list_get(ovAA, 0)) + " f2=" + int_to_str(native_list_get(ovAA, 1)) + " (base GA 730/1090) [PROVISIONAL]")
+    }
+    println("[done] ga-neuron=" + bool_to_str(okga) + " british-neuron=" + bool_to_str(okb1) + " british-2=" + bool_to_str(okb2))
+}
diff --git a/elp/tests/examples/speech-demo.el b/elp/tests/examples/speech-demo.el
new file mode 100644
index 0000000..3336dc5
--- /dev/null
+++ b/elp/tests/examples/speech-demo.el
@@ -0,0 +1,69 @@
+// speech-demo.el - PROOF: Neuron speaks from MEANING, rendered through INGESTED
+// phonetic geometry, own-core, plus voice-by-IMITATION. Built by concatenating
+// the elp realizer + voice-profile + speech-ingest + speech, then this main.
+//
+//   LEARN   : ingest acoustic-phonetics + lexicon SOURCES -> phoneme manifold in
+//             the engram (source -> manifold -> merge).
+//   MEANING : sem_frame("describe","I","Neuron","") -> sem_realize -> "I am Neuron."
+//   PHONES  : words -> phoneme codes, READ from the ingested lexicon geometry.
+//   RENDER  : superpose formant resonances (read from engram) over a glottal
+//             source -> own-core PCM/WAV, in Neuron's own voice.
+//   IMITATE : HEAR a short sample of a different voice -> extract its signature
+//             by ear (autocorrelation pitch + integer-DFT formant) -> render new
+//             speech in that voice. An impression, not a corpus.
+
+fn speak_report(tag: String, codes: [String], voice: [String], pmap: [String], path: String) -> [Int] {
+    let s: [Int] = synth_codes(codes, voice, pmap)
+    let ok: Bool = write_wav(s, 16000, path)
+    println(tag + " samples=" + int_to_str(native_list_len(s)) + " ok=" + bool_to_str(ok) + " -> " + path)
+    return s
+}
+
+fn main() {
+    let outdir: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/"
+
+    // -- LEARN: ingest the speech primitives as geometry --------------------
+    let pmap: [String] = ingest_phonetics("elp/data/phonetics.psv")
+    let lmap: [String] = ingest_lexicon("elp/data/lexicon.psv")
+    let saved: Bool = engram_save(outdir + "phoneme-manifold.json")
+    println("[learn] phonemes=" + int_to_str(native_list_len(pmap) / 2) + " words=" + int_to_str(native_list_len(lmap) / 2) + " manifold_saved=" + bool_to_str(saved))
+
+    // sanity: show that AA's formants came from ingested geometry, not code
+    let aa: [Int] = phon_geo(pmap, "AA")
+    let aaF1: Int = native_list_get(aa, 0)
+    let aaF2: Int = native_list_get(aa, 1)
+    println("[read-geometry] AA F1=" + int_to_str(aaF1) + " F2=" + int_to_str(aaF2) + " (parsed from engram node)")
+
+    // -- MEANING -> WORDS via the realizer's language faculty ----------------
+    let frame: [String] = sem_frame("describe", "I", "Neuron", "")
+    let text: String = sem_realize(frame)
+    println("[meaning->text] " + text)
+
+    // -- WORDS -> PHONEMES (read from ingested lexicon geometry) --------------
+    let codes: [String] = text_phonemes(lmap, text)
+    println("[phonemes] " + list_join(codes, " "))
+
+    // -- RENDER in Neuron's own voice ----------------------------------------
+    let neuron: [String] = voice_neuron()
+    let s1: [Int] = speak_report("[speak neuron]", codes, neuron, pmap, outdir + "neuron.wav")
+
+    // -- IMITATION: hear a distinct voice, recover its signature, re-render ---
+    let vA: [String] = voice_target_a()
+    let hcodes: [String] = native_list_empty()
+    hcodes = native_list_append(hcodes, "SIL")
+    let z: Int = 0
+    while z < 6 {
+        hcodes = native_list_append(hcodes, "AA")
+        z = z + 1
+    }
+    hcodes = native_list_append(hcodes, "SIL")
+    let heard: [Int] = synth_codes(hcodes, vA, pmap)
+    let okh: Bool = write_wav(heard, 16000, outdir + "heard.wav")
+
+    let vB: [String] = voice_analyze(heard, 16000)
+    println("[imitate] heard ACTUAL   f0=" + voice_get(vA, "f0") + " kf=" + voice_get(vA, "kf"))
+    println("[imitate] heard RECOVERED f0=" + voice_get(vB, "f0") + " kf=" + voice_get(vB, "kf") + " (extracted by ear from PCM)")
+    let s2: [Int] = speak_report("[speak imitation]", codes, vB, pmap, outdir + "imitation.wav")
+
+    println("[done] rendered from meaning + ingested geometry; imitation from a heard sample.")
+}
diff --git a/elp/tests/examples/speech-organ-demo.el b/elp/tests/examples/speech-organ-demo.el
new file mode 100644
index 0000000..ec8db5c
--- /dev/null
+++ b/elp/tests/examples/speech-organ-demo.el
@@ -0,0 +1,48 @@
+// speech-organ-demo.el - PROOF: the render now reads its phoneme + accent
+// GEOMETRY from the ingest ORGAN's saved engram files (engram_load +
+// engram_scan_nodes_json + cache), not a same-run hand-load. The British accent
+// is still a composed transform-geometry (voice (+) accent, separable). Numbers
+// come from the organ manifold; the .psv supplies only categorical vowel-class.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // engram-independent caches from source (survive engram_load replacement)
+    let vset: [String] = organ_vset("elp/data/phonetics.psv")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+    // ORGAN read: phonetics FIRST (cache), THEN accent (engram_load replaces store)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let amap: [String] = organ_amap("elp/data/british-accent.engram.json")
+    println("[organ] phon_syms=" + int_to_str(native_list_len(pmap) / 2) + " accent_syms=" + int_to_str(native_list_len(amap) / 2) + " vowels=" + int_to_str(native_list_len(vset)) + " words=" + int_to_str(native_list_len(lmap) / 2))
+
+    // prove the numbers came from the organ node content
+    let g: [Int] = phon_geo(pmap, "AA")
+    println("[organ-read] phoneme AA f1=" + int_to_str(native_list_get(g, 0)) + " f2=" + int_to_str(native_list_get(g, 1)) + " f3=" + int_to_str(native_list_get(g, 2)) + " (P&B1952 MEASURED)")
+    let ov: [Int] = accent_formants(amap, "AA")
+    if native_list_len(ov) >= 3 {
+        println("[organ-read] accent AA(LOT) f1=" + int_to_str(native_list_get(ov, 0)) + " f2=" + int_to_str(native_list_get(ov, 1)) + " (DERIVED RP, PROVISIONAL)")
+    }
+    println("[organ-read] non_rhotic=" + int_to_str(is_nonrhotic(amap)))
+
+    let neuron: [String] = voice_neuron()
+    let noacc: [String] = native_list_empty()
+
+    // Sentence 1: "I am Neuron." from meaning; GA vs RP = separable toggle
+    let t1: String = sem_realize(sem_frame("describe", "I", "Neuron", ""))
+    let c1: [String] = text_phonemes(lmap, t1)
+    println("[s1] " + t1 + " :: " + list_join(c1, " "))
+    let ga: [Int] = synth_codes_accent(c1, neuron, pmap, noacc, vset)
+    let okga: Bool = write_wav(ga, 16000, outdir + "ga-neuron-organ.wav")
+    let br1: [Int] = synth_codes_accent(c1, neuron, pmap, amap, vset)
+    let okb1: Bool = write_wav(br1, 16000, outdir + "british-neuron-organ.wav")
+
+    // Sentence 2: non-rhoticity showcase
+    let t2: String = sem_realize(sem_frame("describe", "I", "here", ""))
+    let c2: [String] = text_phonemes(lmap, t2)
+    let c2rp: [String] = apply_rhoticity(c2, vset)
+    println("[s2] " + t2 + " :: GA=" + list_join(c2, " ") + "  RP=" + list_join(c2rp, " "))
+    let br2: [Int] = synth_codes_accent(c2, neuron, pmap, amap, vset)
+    let okb2: Bool = write_wav(br2, 16000, outdir + "british-2-organ.wav")
+
+    println("[done] ga-organ=" + bool_to_str(okga) + " british-organ=" + bool_to_str(okb1) + " british-2-organ=" + bool_to_str(okb2))
+}
diff --git a/elp/tests/examples/speech-voice-demo.el b/elp/tests/examples/speech-voice-demo.el
new file mode 100644
index 0000000..2ade2ec
--- /dev/null
+++ b/elp/tests/examples/speech-voice-demo.el
@@ -0,0 +1,31 @@
+// speech-voice-demo.el - LIVE VOICE LOOP (stand-in test). Capture -> voiceprint
+// -> reshape -> INGEST AS GEOMETRY -> read the target back FROM geometry -> the
+// EL projector renders a line reaching for that voice. Stand-in "Will" = the
+// voiceprint of imitation.wav. HONEST: pitch + coarse vocal-tract scale, NOT a clone.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+    let vp: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/will-voiceprint.json"
+
+    // 1+2: reshape voiceprint JSON -> organ voice-signature source
+    let sig0: [String] = native_list_empty()
+    let sig: [Int] = reshape_voiceprint(vp, "elp/data/will-voice.json")
+    // 3: ingest as geometry + engram_save a reloadable manifold file
+    let ig: Int = ingest_voice(sig, "elp/data/will-voice.engram.json")
+    // 4: READ the target back FROM geometry (engram_load + scan + filter)
+    let g: [Int] = load_voice("elp/data/will-voice.engram.json")
+    println("[voice-geometry] read from manifold: f0=" + int_to_str(native_list_get(g, 0)) + " f0_end=" + int_to_str(native_list_get(g, 1)) + " kf=" + int_to_str(native_list_get(g, 2)) + " f1=" + int_to_str(native_list_get(g, 3)) + " f2=" + int_to_str(native_list_get(g, 4)) + " f3=" + int_to_str(native_list_get(g, 5)) + " (measured, COARSE — not a clone)")
+
+    // phoneme geometry from the organ (loaded AFTER the voice sig is cached in EL)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // 5: render a line FROM MEANING in Will's voice
+    let vw: [String] = voice_will(native_list_get(g, 0), native_list_get(g, 1), native_list_get(g, 2))
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " ") + "  in voice=will f0=" + int_to_str(voice_get_int(vw, "f0")) + " kf=" + int_to_str(voice_get_int(vw, "kf")))
+    let samples: [Int] = synth_codes(codes, vw, pmap)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply.wav")
+    println("[done] will-reply.wav=" + bool_to_str(ok))
+}
diff --git a/elp/tests/examples/speech-voice-demo2.el b/elp/tests/examples/speech-voice-demo2.el
new file mode 100644
index 0000000..f339a7b
--- /dev/null
+++ b/elp/tests/examples/speech-voice-demo2.el
@@ -0,0 +1,48 @@
+// speech-voice-demo2.el - LIVE VOICE LOOP on Will's richer 30s read, with a
+// GEOMETRIC SET-REPLACE of the voice_will manifold (supersede the coarse 10s
+// region, insert the 30s region — no duplicate node, no per-node CRUD; Will's
+// standing rule f999c5ff). HONEST: 30s steadies the 11-number average over more
+// of his vowels, but it is still one formant triple with no coarticulation or
+// prosody — closer but still synthetic, not a clone.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+    let vp: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/will30-voiceprint.json"
+    let manifest: String = "elp/data/will-voice.engram.json"
+
+    // --- SET-REPLACE step 1: read the PRIOR region (text read of the manifold
+    //     file — no engram_load, so the store stays clean) and report what is
+    //     being superseded. ---
+    let prior: String = fs_read(manifest)
+    let pp: Int = str_index_of(prior, "voice will ")
+    if pp >= 0 {
+        let pw: String = str_slice(prior, pp, pp + 200)
+        println("[set-replace] superseding PRIOR voice region: f0=" + int_to_str(parse_uint_from(pw, "f0=")) + " kf=" + int_to_str(parse_uint_from(pw, "kf=")) + " f1=" + int_to_str(parse_uint_from(pw, "f1=")))
+    }
+
+    // --- step 2: reshape the 30s voiceprint -> organ voice-signature source ---
+    let sig: [Int] = reshape_voiceprint(vp, "elp/data/will-voice.json")
+
+    // --- step 3: INSERT the fresh 30s region into an EMPTY engram and save ->
+    //     wholesale replaces the manifold file (old region dropped, not edited,
+    //     not duplicated). This is the geometric set-replace. ---
+    let ig: Int = ingest_voice(sig, manifest)
+
+    // --- step 4: READ the new target BACK from geometry ---
+    let g: [Int] = load_voice(manifest)
+    println("[voice-geometry] new region read from manifold: f0=" + int_to_str(native_list_get(g, 0)) + " f0_end=" + int_to_str(native_list_get(g, 1)) + " kf=" + int_to_str(native_list_get(g, 2)) + " f1=" + int_to_str(native_list_get(g, 3)) + " f2=" + int_to_str(native_list_get(g, 4)) + " f3=" + int_to_str(native_list_get(g, 5)) + " (measured 30s, COARSE — not a clone)")
+
+    // phoneme + lexicon geometry from the organ (loaded after the voice sig is
+    // cached in EL, since engram_load replaces the store)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // --- step 5: render a fresh reply FROM MEANING in the 30s Will voice ---
+    let vw: [String] = voice_will(native_list_get(g, 0), native_list_get(g, 1), native_list_get(g, 2))
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " ") + "  in voice=will f0=" + int_to_str(voice_get_int(vw, "f0")) + " kf=" + int_to_str(voice_get_int(vw, "kf")))
+    let samples: [Int] = synth_codes(codes, vw, pmap)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply2.wav")
+    println("[done] will-reply2.wav=" + bool_to_str(ok))
+}
diff --git a/elp/tests/examples/speech-voicegeom-demo.el b/elp/tests/examples/speech-voicegeom-demo.el
new file mode 100644
index 0000000..afde5a6
--- /dev/null
+++ b/elp/tests/examples/speech-voicegeom-demo.el
@@ -0,0 +1,37 @@
+// speech-voicegeom-demo.el - THE JUMP: render Will's VOWEL SPACE + PROSODY
+// (measured over 30s), not the single 11-number average. His vowels land at HIS
+// targets; pitch follows HIS melody. All read back FROM the ingested geometry.
+// INTERIM: the geometry was Python-measured (measure_voice.py, numpy LPC/F0) —
+// to be superseded by the engram-measures-audio path. No source layer.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // 1: ingest vowel space + prosody as geometry (empty store -> save; set-replace)
+    let ig: Int = ingest_voicegeom("elp/data/will-vowelspace.psv", "elp/data/will-prosody.psv", "elp/data/will-voicegeom.engram.json")
+    // kf (vocal-tract scale for consonants) from the earlier will-voice manifold
+    let sigv: [Int] = load_voice("elp/data/will-voice.engram.json")
+    let kf: Int = native_list_get(sigv, 2)
+    // 2: read vowel space + prosody back FROM geometry
+    let vmap: [String] = load_voicegeom("elp/data/will-voicegeom.engram.json")
+    let pros: [Int] = prosody_from(vmap)
+    println("[geometry] vowels=" + int_to_str((native_list_len(vmap) - 2) / 2) + " prosody f0_median=" + int_to_str(native_list_get(pros, 0)) + " f0_min=" + int_to_str(native_list_get(pros, 1)) + " f0_max=" + int_to_str(native_list_get(pros, 2)) + " kf=" + int_to_str(kf))
+    let ehv: [Int] = vmap_get(vmap, "EH")
+    let ihv: [Int] = vmap_get(vmap, "IH")
+    println("[his-vowels] EH=" + int_to_str(native_list_get(ehv, 0)) + "/" + int_to_str(native_list_get(ehv, 1)) + " IH=" + int_to_str(native_list_get(ihv, 0)) + "/" + int_to_str(native_list_get(ihv, 1)))
+
+    // phoneme geometry from the organ (loaded AFTER caches are in EL)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // 3+4: render FROM MEANING in his-vowels + his-prosody voice
+    let vw: [String] = voice_will(native_list_get(pros, 0), native_list_get(pros, 1), kf)
+    let noacc: [String] = native_list_empty()
+    let novset: [String] = native_list_empty()
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " "))
+    let samples: [Int] = synth_codes_accent(codes, vw, pmap, noacc, novset, vmap, pros)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply3.wav")
+    println("[done] will-reply3.wav=" + bool_to_str(ok))
+}