PLN's validation idea: famous tracks whose feeling/structure/genre the model already
knows = pseudo-ground-truth to grade the emotion engine, disagreements driving tuning +
coverage (self-correcting 30→100→300). emotion_corpus_gt.json: wave-1 22 tracks spanning
all 12 emotions + V/A quadrants + genres + structural archetypes (steady / quiet-loud /
long-build); GT = emotion labels + V/A centre + approx fractional section structure +
genre. emotion_corpus.py: fetch (yt-dlp android+bestaudio 403 workaround) → analyze
(demucs+stem-aware or --mix) → grade (emotion-hit, V/A error, quadrant, vocal-presence) →
gaps (coverage histogram → next wave).

BASELINE (whole-mix, 5 tracks): 40% emotion-hit, 40% quadrant-hit, mean V/A err 0.75 —
honest verdict: NOT yet trustworthy. Three systematic biases surfaced: (1) center/positive
compression (extremes pulled to mild +V/+A), (2) 'playful' over-fires (3/5), (3) whole-mix
misses aggression entirely (Prodigy Firestarter→playful +V) — the exact case stem-aware
(drums/bass arousal) should fix. Corpus now drives calibration before the #90 hexa handoff.
gaps: +- and -- quadrants thin, fill at wave 30.