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

arXiv: https://arxiv.org/abs/2609.26125 · HTML: https://mlsanity.com/paper/2609.26125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics](https://mlsanity.com/paper/2411.07186/) (2024)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [Spatial Mixture-of-Experts](https://mlsanity.com/paper/2211.13491/) (2022)
- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)

## Abstract (arXiv)

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 predict the two hydrophone models at 74.1% and 85.6% balanced accuracy, respectively, but hydrophone model is strongly confounded with season and other deployment-level acquisition differences. On a retrospectively screened and capped benchmark of 54m507 recordings, repeated station-grouped holdout yields an ROC-AUC of 0.612 with a station-bootstrap 95% interval of 0.582 to 0.648, compared with 0.661 under a random-window diagnostic. Their paired difference is 0.049 (0.040 to 0.056). Removing raw energy changes unseen-station ROC-AUC from 0.612 to 0.603, while an exploratory training-station scale analysis is non-monotonic. These results show that random-window validation overstates transfer to unseen station identifiers in this corpus. They support dependence-aware validation and broader independent spatial sampling, while spatial-expert models remain a hypothesis rather than an established remedy.