feat(heads): auto-apply observability + on by default (#114 auto-apply B)

Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration
0059 + model default flipped. The support (>=30) + measured-precision gates keep
it safe and every auto-tag is reversible.

Observability so the operator can tune from real data:
- MISFIRE = an auto-applied (source='head_auto') tag the operator later removes.
  UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it).
  Both captured at correction time in TagService.add_to_image/remove_from_image
  (source is lost on delete) into durable per-tag counters (head_metric), keyed
  by tag so they survive head retrain/prune.
- Daily snapshot_head_metrics writes a per-concept time-series point
  (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires
  + head quality; 180-day retention; daily beat.
- GET /api/heads/metrics: per-concept current counts + realized misfire rate +
  head quality, plus the snapshot time-series — the report to tune the precision
  target + support floor.

Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual
removal isn't a misfire, headless manual add isn't an under-fire), snapshot
time-series, metrics API.

What's the autofire threshold? There's no single number — each graduated head
derives its OWN probability cutoff from its PR curve: the operating point that
holds precision >= head_auto_apply_precision (0.97) at max recall. The global
knobs are that target + the >=30 support floor.

NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

backend/app/models/__init__.py

@@ -9,6 +9,8 @@ from .credential import Credential
 from .download_event import DownloadEvent
 from .external_link import ExternalLink
 from .head_auto_apply_run import HeadAutoApplyRun
+from .head_metric import HeadMetric
+from .head_metrics_snapshot import HeadMetricsSnapshot
 from .head_training_run import HeadTrainingRun
 from .image_prediction import ImagePrediction
 from .image_provenance import ImageProvenance
@@ -69,6 +71,8 @@ __all__ = [
     "LibraryAuditRun",
     "MLSettings",
     "HeadAutoApplyRun",
+    "HeadMetric",
+    "HeadMetricsSnapshot",
     "HeadTrainingRun",
     "TagAlias",
     "TagAllowlist",