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/head_metrics_snapshot.py

@@ -0,0 +1,38 @@
+"""HeadMetricsSnapshot — a daily per-concept time-series point (#114).
+
+The "amount of change over time" reporting the operator asked for: once a day,
+record each concept's auto-applied VOLUME (current head_auto tags), cumulative
+misfires/under-fires, and the head's measured quality. Plotting these rows over
+time shows whether auto-apply is landing better/worse and whether tagging more is
+sharpening a concept — the signal for tuning the precision target + support floor.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, Float, ForeignKey, Integer, String, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class HeadMetricsSnapshot(Base):
+    __tablename__ = "head_metrics_snapshot"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), index=True
+    )
+    # Denormalized so a snapshot stays readable even if the tag is later renamed.
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    snapshot_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(), index=True
+    )
+    # Current count of source='head_auto' applications still standing.
+    n_auto_applied: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    n_misfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    n_underfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    # The head's measured quality at snapshot time (null if no head exists).
+    ap: Mapped[float | None] = mapped_column(Float, nullable=True)
+    precision_cv: Mapped[float | None] = mapped_column(Float, nullable=True)
+    recall: Mapped[float | None] = mapped_column(Float, nullable=True)
+    n_pos: Mapped[int | None] = mapped_column(Integer, nullable=True)