Gabriel Spadon

Physics-informed vessel trajectory prediction via finite difference kinematic losses

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 prio...

Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling

EddyFlow is a representation learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization and demonstrates that physics-informed representation learning reduces zero-shot RMSE by 21%, achieves up to 85.6% skill relat...

Covariance Last-Layer Ensembles: Function-Space Diversity for Efficient Uncertainty Quantification

Viewing OC as a last-layer ensemble also organizes detectors into a two-axis taxonomy and exposes the OC score as a magnitude, motivating a scale-invariant, label-free direction score that repairs its near-OOD failure.