Benchmarking Models for Ocean Wind Speed Estimation Based on Passive Acoustic Monitoring
Bibliographic record
Abstract
Passive acoustic monitoring is a promising tool for long-term ocean observations, offering a unique means to capture physical and biological processes. This study explores its potential as a source of fine temporal scale in-situ wind speed product by assembling a unique corpus of acoustic datasets co-located with or near in-situ weather stations. This study offers two key contributions: i) setting up a benchmarking framework for the development and evaluation of models in acoustic meteorology, and ii) applying this framework to assess the performance of various models, comparing parameters from the literature with those trained on datasets from this study's corpus. Regarding the latter point, results show that most untrained models fail to generalize due to the intrinsic variability of soundscape in different basins and environmental conditions, as well as calibration inaccuracies. However, all models can achieve satisfactory performance on specific datasets after training. Incorporating diverse observational sources, such as gliders and BGC-Argo floats, could enhance model robustness, and improved acoustic-based estimates will help refine satellite-derived wind products and numerical weather predictions, ultimately advancing global wind field modeling and air-sea interaction research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".