Three live measurement windows and two consensus-layer fixes (#125, #126)
converged on one decision (CORRELATION_REVIEW_0907b.md, _0912.md): stop
iterating the correlator's consensus layer, the actual lever is upstream —
a classifier in scene extraction that recognizes radio housekeeping (roll
call, bare 10-4/10-8/98 acknowledgements, unit check-ins) before it ever
becomes a scene for the correlator to judge.
Adds app/internal/chatter_classifier.py: a pure classify_chatter(transcript)
function recognizing two shapes drawn from hand-labeled examples in the
review docs, cross-referenced against the real dumps — not invented regexes.
Deliberately conservative: anything that doesn't cleanly reduce to a known
shape returns (False, None) and the existing pipeline runs unchanged.
SHADOW MODE ONLY. intelligence.extract_scenes computes the verdict next to
the existing _is_garbage_transcript / transcript_too_short gates and writes
chatter_classifier_verdict / chatter_classifier_reason onto the call doc,
but does not skip extraction. admin.py's correlation-debug _call_summary
surfaces both fields, same pattern as corr_gate_veto (#115/#126), so the
next live window can measure the real-world false-positive rate before
anything is wired to actually skip extraction. TODO(server-26#127) marks
the call site.
Backtest against all three existing dumps (1002 calls): 154 flagged, 0
false positives (no flagged call carries tags, coords, non-routine severity,
or matches any review-doc-named dangerous-to-drop transcript — the major
extinguishing-fire call, geocoded calls, pursuit updates, the Pelham Station
subject check, the property-retrieval call, all individually verified).
tests/test_chatter_classifier.py: real transcripts from the dumps/review
docs in both directions. Sandboxed pytest 332 -> 364, green.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Tbknwttzou4s46PAykmtix
