Near real-time marine mammal monitoring from gliders: Practical challenges, system development, and management implications
Bibliographic record
Abstract
In 2017, an endangered North Atlantic right whale mortality event in the Gulf of St. Lawrence, Canada, triggered the implementation of dynamic mitigation measures that required real-time information on whale distribution. Underwater glider-based acoustic monitoring offers a possible solution for collecting near real-time information but has many practical challenges including self-noise, energy restrictions, and computing capacity, as well as limited glider-to-shore data transfer bandwidth. This paper describes the development of a near real-time baleen whale acoustic monitoring glider system and its evaluation in the Gulf of St. Lawrence in 2018. Development focused on identifying and prioritizing important acoustic events and on sending contextual information to shore for human validation. The system performance was evaluated post-retrieval, then the trial was simulated using optimized parameters. Trial simulation evaluation revealed that the validated detections of right, fin, and blue whales produced by the system were all correct; the proportion of species occurrence missed varied depending on the timeframe considered. Glider-based near real-time monitoring can be an effective and reliable technique to inform dynamic mitigation strategies for species such as the North Atlantic right whale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".