When Big Data Becomes a Curse: Spatial Heterogeneity and the Limits of Learning from Passive Acoustic Monitoring Data
Passive Acoustic Monitoring produces large archives whose recordings are clustered by deployment, season, station identifier, and acquisition configuration. We analyze 908,072 AIS-labeled 679-second recordings from 38 deployments, 20 Atlantic Canadian station identifiers, and 21 receiver positions. The AIS-contact prior varies by more than 200-fold, and per-deployment screening distributions require local interpretation. Bidirectional cross-season transfer over the 18 station identifiers observed in both seasons predicts station identity above the 5.56% uniform-chance level, with balanced accuracy of 15.9% for AIS-contact and 16.4% for no-AIS-contact recordings. The same descriptors predi...
| Authors | Gabriel Spadon, Wayne Renaud, Priyanka Aravindan |
|---|---|
| Primary category | cs.AI |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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