Three tools for the unattended ~6h night run that scales the emotion GT corpus
and validates it across models:

- emotion_corpus_merge.py — fold model-authored GT batches into the corpus
  SAFELY: validate vs the fixed 12-emotion vocab + V/A range, dedup by slug AND
  by normalized artist|title (no track twice under two slugs), default the
  yt-dlp query. Idempotent.
- emotion_overnight.py — disk-safe (volume at 97%%), deadline-bounded grind:
  phase 1 fetch→whole-mix→delete mp3 over the whole corpus (bank the cheap
  signal first); phase 2 re-fetch→demucs stems→delete for as many as the
  deadline allows (grows the #92 stem evidence base). Per-track try/except,
  yt-dlp throttle + consecutive-failure backoff, audio purged after every track.
- emotion_drift.py — cross-model agreement on the GT itself: a second model
  (sonnet) labels the same tracks; high opus-vs-sonnet V/A drift or quadrant
  mismatch flags UNCERTAIN ground truth to down-weight, not trust. Single-model
  GT is a blind spot — a second pair of ears finds it.

Spirit of PLN's overnight brief: scale the dataset (the lever), and check
cross-model drift instead of trusting one model's priors.