intelligence: shadow-mode upstream dispatch-vs-chatter classifier (server-26#127)

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

drb-c2-core/app/internal/intelligence.py

@@ -16,6 +16,7 @@ from typing import Optional
 from app.internal.logger import logger
 from app.internal import firestore as fstore
 from app.internal import area_context
+from app.internal.chatter_classifier import classify_chatter
 # Location validity is defined once, by the module that owns the incident's
 # location/pin invariant.  incident_correlator does not import this module, so
 # this is not a cycle.
@@ -218,6 +219,26 @@ async def extract_scenes(
             pass
         return []
 
+    # server-26#127 — SHADOW MODE ONLY. Computes whether this transcript looks
+    # like non-event radio housekeeping (roll call, bare 10-4/10-8/98
+    # acknowledgements, unit check-ins) and records the verdict on the call
+    # doc, but does NOT skip extraction — the scene-extraction call below runs
+    # exactly as it does today regardless of what this says. This is step one
+    # of getting live production data on the classifier's false-positive rate
+    # before trusting it with anything real; see CORRELATION_REVIEW_0912.md.
+    # TODO(server-26#127): flip this from shadow to live (skip extraction and
+    # write skip_reason="non_event_chatter" instead of just recording the
+    # verdict) once a live shadow-mode window confirms 0 false positives on
+    # real production traffic.
+    chatter_is_chatter, chatter_reason = classify_chatter(transcript)
+    try:
+        await fstore.doc_set("calls", call_id, {
+            "chatter_classifier_verdict": chatter_is_chatter,
+            "chatter_classifier_reason":  chatter_reason,
+        })
+    except Exception:
+        pass
+
     raw_scenes: list[dict] = await asyncio.to_thread(
         _sync_extract,
         transcript, talkgroup_name, talkgroup_id, system_id, segments, vocabulary, ten_codes,