Reject an extracted auto-term when its label document frequency exceeds
node_count/400 (floor 8) -- measured live at 12,859 nodes, threshold 32.
Live label df separates the classes by an order of magnitude:
rejected: <!--:220 SELF:175 Engram:125 CORE:88 STAR:36
passed: Dual:12 Sparse:8 Latent:6 MemQ:1 dGRPO:1 engram_goal_bias:1
Verified against the running soul (boot 21). Peak curiosity activation fell
from 541 to 113; the flood terms (SELF, CORE, Engram, STAR, <!--) are absent
from post-fix scans while topical compound identifiers pass untouched.
Sample is 7 scans -- suggestive, not conclusive; watch the next review.
Nested conditional rather than max(): El let is single-assignment, so the
floor is expressed as a second conjunct.
Verification note: content df was tested as an alternative signal and
rejected -- 'Curiosity' has the highest content df in the store (5526) yet
one of the lowest activation counts (113). Label df is the correct field
because label is what the first-word extractor reads.
will.anderson
21710d5c8e