correlator: judge each scene on its own embedding + severity (server-26#80, #95)

intelligence.py writes only the primary scene's embedding and severity to
calls/{id}. _build_context read them back off the call doc, so every
non-primary scene of a multi-scene call was correlated against scene 1's
semantic vector and severity rung: a scene about a different event scored
on the embedding path against the wrong incident, and could inherit a
minor/moderate/major severity it never had, clearing the creation gate on
borrowed weight. Same defect and same fix as the #87 coords leak.

- _build_context / preview_correlation / correlate_call: take embedding and
  severity as params; drop the call_doc.get() fallbacks. A scene that
  passes none has none, and is judged thin on its own signal.
- upload.py: both scene loops pass scene["embedding"] / scene["severity"];
  _correlate_with_consensus forwards them. The no-scene unclassified branch
  passes neither (correct: no scene, judged thin).
- recorrelation_sweep: passes the call doc's stored values explicitly
  (whole-call re-link, link-only, so a borrowed severity cannot create).
- intelligence.py: SCENE DETECTION prompt tightened toward one scene
  (server-26#5, partial) - MULTIPLE only for genuinely separate events,
  "when unsure, one scene", plus a not-a-new-scene list.
- test_incident_identity.py: +2 regression tests mirroring the #87 test.

Full c2-core suite green (295 passed). #5 prompt change is unmeasured -
needs a scoped correlation-only window. Known remaining legs, tracked
separately: llm_correlator._call_block still reads the whole-call
transcript per scene; content-divergence veto skips on a None embedding.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

drb-c2-core/app/routers/upload.py

@@ -114,6 +114,8 @@ async def _correlate_with_consensus(
     vehicles: Optional[list] = None,
     cleared_units: Optional[list] = None,
     reassignment: bool = False,
+    embedding: Optional[list] = None,
+    severity: Optional[str] = None,
 ) -> Optional[str]:
     """
     Consensus correlator: runs the rules engine and the cheap LLM in sequence.
@@ -131,6 +133,7 @@ async def _correlate_with_consensus(
         tags=tags, incident_type=incident_type, location=location,
         location_coords=location_coords, units=units, vehicles=vehicles,
         cleared_units=cleared_units, reassignment=reassignment,
+        embedding=embedding, severity=severity,
     )
     ctx             = preview["ctx"]
     rules_decision  = preview["decision"]
@@ -221,6 +224,8 @@ async def _run_extraction_pipeline(
                 vehicles=scene.get("vehicles"),
                 cleared_units=scene.get("cleared_units"),
                 reassignment=is_reassignment,
+                embedding=scene.get("embedding"),
+                severity=scene.get("severity"),
             )
             if incident_id and incident_id not in incident_ids:
                 incident_ids.append(incident_id)
@@ -336,6 +341,8 @@ async def _run_intelligence_pipeline(
                 vehicles=scene.get("vehicles"),
                 cleared_units=scene.get("cleared_units"),
                 reassignment=is_reassignment,
+                embedding=scene.get("embedding"),
+                severity=scene.get("severity"),
             )
             if incident_id and incident_id not in incident_ids:
                 incident_ids.append(incident_id)