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
bvandeusen
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