Gliders for passive acoustic monitoring of the oceanic environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ocean gliders are quiet, buoyancy-driven, long-endurance, profiling autonomous platforms. Gliders therefore possess unique advantages as platforms for Passive Acoustic Monitoring (PAM) of the marine environment. In this paper, we review available glider platforms and passive acoustic monitoring systems, and explore current and potential uses of passive acoustic monitoring-equipped gliders for the study of physical oceanography, biology, ecology and for regulatory purposes. We evaluate limiting factors for passive acoustic monitoring glider surveys, such as platform-generated and flow noise, weight, size and energy constraints, profiling ability and slow movement. Based on data from 34 passive acoustic monitoring glider missions, it was found that <13% of the time spent at sea was unsuitable for passive acoustic monitoring measurements, either because of surface communications or glider manoeuvre, leaving the remainder available for subsequent analysis. To facilitate the broader use of passive acoustic monitoring gliders, we document best practices and include workarounds for the typical challenges of a passive acoustic monitoring glider mission. Three research priorities are also identified to improve future passive acoustic monitoring glider observations: 1) Technological developments to improve sensor integration and preserve glider endurance; 2) improved sampling methods and statistical analysis techniques to perform population density estimation from passive acoustic monitoring glider observations; and 3) calibration of the passive acoustic monitoring glider to record absolute noise levels, for anthropogenic noise monitoring. It is hoped this methodological review will assist glider users to broaden the observational capability of their instruments, and help researchers in related fields to deploy passive acoustic monitoring gliders in their studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it