feat(ml): soft auto-apply — retract auto-tags now below threshold (milestone 139)

Daily scheduled_retract_auto_tags re-scores standing auto-applied tags and drops
the ones the model no longer supports:
- retract_auto_applied_heads: per graduated head, re-score its source='head_auto'
  images (bounded — only the images already carrying the auto-tag, not the whole
  library) and remove ones now < auto_apply_threshold.
- retract_auto_applied_ccip: per source='ccip_auto' character tag, max-cosine the
  image's figure vectors vs that character's prototypes; remove ones now below the
  ccip auto-apply threshold.
Both SKIP operator-confirmed tags (TagPositiveConfirmation) and are SILENT — a low
score isn't proof the tag was wrong, so no hard negative is recorded (that's
reserved for an operator removal). No-op unless the relevant auto-apply switch is
on. New daily beat. sklearn-free tests for both paths + the disabled no-op.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

backend/app/tasks/ml.py

@@ -592,3 +592,23 @@ def scheduled_ccip_auto_apply() -> str:
                     applied += 1
             session.commit()
         return f"applied={applied}"
+
+
+@celery.task(
+    name="backend.app.tasks.ml.scheduled_retract_auto_tags",
+    soft_time_limit=1800, time_limit=2100,
+)
+def scheduled_retract_auto_tags() -> str:
+    """Soft auto-apply (milestone 139): retract standing head_auto/ccip_auto tags
+    the model no longer supports (score now below the auto-apply threshold),
+    skipping operator-confirmed ones. Silent (no hard negative). No-op unless the
+    respective auto-apply switch is on. Returns 'head=N ccip=M'."""
+    from ..services.ml.character_prototypes import retract_auto_applied_ccip
+    from ..services.ml.heads import retract_auto_applied_heads
+
+    SessionLocal = _sync_session_factory()
+    with SessionLocal() as session:
+        n_head = retract_auto_applied_heads(session)
+    with SessionLocal() as session:
+        n_ccip = retract_auto_applied_ccip(session)
+    return f"head={n_head} ccip={n_ccip}"