Near real-time passive acoustic detection and reporting of marine mammals from mobile autonomous platforms
Bibliographic record
Abstract
Over the past two decades, passive acoustic monitoring has proven to be an effective means of estimating the occurrence of marine mammals. The vast majority of applications involve archival recordings from bottom-mounted instruments or towed hydrophones from moving ships; however, there is growing interest in assessing marine mammal occurrence from autonomous platforms, particularly in real time. The Woods Hole Oceanographic Institution has developed the capability to detect, classify, and remotely report in near real time the calls of marine mammals via passive acoustics from a variety of long-endurance autonomous platforms, including Slocum gliders, wave gliders, and moored buoys. The mobile Slocum and wave gliders provide marine mammal occurrence information in near real-time over spatial scales of hundreds to thousands of kilometers and temporal scales of a few months. Buoys and Slocum gliders are now being used regularly to accurately monitor baleen whales in near real-time off the east coasts of the U.S. and Canada. Our long-range goal is to incorporate this capability into regional and global ocean observatory initiatives to (1) improve marine mammal conservation and management and (2) study changes in marine mammal distribution over multi-annual time scales in response to climate change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".