When Big Data Becomes a Curse: Spatial Heterogeneity and the Limits of Learning from Passive Acoustic Monitoring Data

arXiv:2609.26125 · cs.AI, cs.SD · Submitted 2026-08-06 · Read on arXiv

cs.AI, cs.SD

Submitted: 2026-08-06

Updated: 2026-08-06

Code: https://github.com/gabrielspadon/scotian-shelf-labeling

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 23× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Provenance — 4× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• AIS Data — 2× in this paper · explained in 1 episode
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Automatic Identification System — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes

Sources

• Scaling Laws for Neural Language Models
• NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics

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