Marine soundscape monitoring from underwater autonomous vehicles—Passive acoustic monitoring gliders
Bibliographic record
Abstract
Ocean gliders are buoyancy-driven autonomous underwater platforms, able to collect oceanographic measurements along vertical profiles during multi-months missions, covering thousands of kilometers. They glide quietly through the water column without propulsion noise and are therefore extremely suitable for Passive Acoustic Monitoring (PAM) of the marine environment. From PAM glider data in the Mediterranean Sea and the Southern Ocean, we illustrate the current and potential uses of PAM gliders for the study of physical oceanography, biology, ecology and for regulatory purposes. We evaluate limiting factors for PAM glider survey, such as platform-generated and flow noise, instrument size and power constraints, profiling ability and movement of the platform. We provide recommendations and good practices for typical PAM glider surveys and present future developments identified by the PAM glider community to further develop the readiness level and societal impact of PAM glider observation: (1) Calibration of the PAM glider to collect absolute sound levels; (2) adapted sampling methods and statistical analysis techniques to perform population density estimation; and (3) Integration of PAM glider observation to existing monitoring programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".