feat(risk-monitor): measure the rule that fires, and give fundamentals their own channel

The Warning study measured a fitted percentile crossing that nothing consumes.
What reaches Telegram is a quadrant change: fixed 50/40 dividers, hysteresis,
two-session confirmation, 3-day cooldown. Those thresholds are constants, not
fits, so there is no training set to protect and all 11 detected corrections are
evaluable instead of the 4 that fell in a holdout.

Replaying it: 1/10 corrections, 0.9 false alarms/year. Random alarms at the same
firing rate match or beat that in 65% of draws. The panel now carries ablations
(does the quadrant machinery earn its place?), external baselines (does the score
earn its complexity?), and that null, because a bare "2 of 4" was unreadable in
either direction. Nothing in the alert path was retuned on the strength of it.

Fundamentals become a third channel rather than a term in either score. v3 cut
them arguing 12+8 of 100 points "could not change any published conclusion" --
true only when every technical sensor reads zero; weighted they moved the bar for
the 40 divider from 40 to 25. But no fusion weight is measurable either: with ~10
events and no fundamental history, any weight is a policy preference presented as
a measurement. So the read is a categorical state (supportive/neutral/adverse/
unknown) with an evidence grade, derived by fixed rules from stored facts, read
by confluence. The LLM extracts and explains; it does not score.

Absence stays absence throughout. `unknown` is unreachable by averaging, a stale
or empty observation may display but never confirm, extraction failures map to
`unknown` rather than `mixed`, and the study rows are coverage-matched and marked
not-measurable until enough corrections are covered -- otherwise a fortnight of
observations renders as 0/10 and reads as a failed test.

Observations become a real time series (migration 033); they lived in a single
overwritten settings slot, so no history existed to replay. Pre-rename snapshots
are adapted rather than discarded. METHODOLOGY stays v4 -- no score changed --
so no reseed; STUDY_SCHEMA moves to 3 and discards the cached report.

Post-deploy: re-run Event Study from Admin -> Jobs. The panel reads "not run yet"
until then.

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

app/scheduler.py

@@ -1351,8 +1351,11 @@ async def run_event_study_job() -> None:
             report = await run_event_study_and_store(db)
 
         _runtime_progress(job_name, processed=1, total=1)
+        shipped = report.get("shipped") or {}
         if report.get("available"):
-            metrics = report.get("metrics") or {}
+            # The shipped quadrant rule is the headline; the fitted-threshold
+            # variant lives under report["fitted"] and is not what fires.
+            metrics = shipped.get("metrics") or {}
             msg = (
                 f"{metrics.get('events_warned', 0)}/{metrics.get('events', 0)} warned, "
                 f"{metrics.get('false_alarms_per_year', 0)} false alarms/year"
@@ -1360,7 +1363,10 @@ async def run_event_study_job() -> None:
         else:
             msg = report.get("reason", "no data")
         _runtime_finish(job_name, "completed", processed=1, total=1, message=msg)
-        _log_event(logging.INFO, "job_complete", job=job_name, events=len(report.get("events", [])))
+        _log_event(
+            logging.INFO, "job_complete", job=job_name,
+            events=len(shipped.get("events") or []),
+        )
     except Exception as exc:
         _runtime_finish(job_name, "error", processed=0, total=1, message=str(exc))
         _log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))