Tracking whales on the Scotian Shelf using passive acoustic monitoring on ocean gliders
Bibliographic record
Abstract
Expanded marine shipping and industrial activity has increased the risk of harmful effects on marine mammals. Quantitative estimates of marine mammal time and space distributions are essential for developing mitigation strategies designed to reduce the risks. Seasonal distributions of key marine mammals can be estimated by deploying passive acoustic monitoring (PAM) hydrophone systems and using the acoustic data to monitor, detect and identify species presence, often in near real-time. Most contemporary PAM deployments in the ocean are stationary and archive the acoustic data for post-recovery analyses after some extended period and are thus not ideal for addressing risk dynamics in near real-time. Substantive expansions of fixed PAM arrays over large ocean expanses can be economically and on-time limiting. Mobile autonomous vehicles now offer the economy of collecting the necessary acoustic and oceanographic data over extended periods and across large swaths of the ocean. They can operate with a high degree of spatial sampling flexibility in near real-time that cannot be easily achieved using fixed PAM arrays. The Whale Habitat and Listening Experiment (WHaLE), funded by the Marine Environmental Observation Prediction And Response Network (MEOPAR) at Dalhousie University, and using Ocean Tracking Network (OTN) autonomous vehicles, is searching for whale habitats and monitoring the distributional patterns of the endangered North Atlantic right whale and other at-risk baleen whales across the shelf waters of Atlantic Canada. This is being achieved through fixed PAM array deployments involving several research partners, as well as the deployment of profiling and surface gliders (autonomous vehicles) equipped with PAM systems capable of detecting and identifying baleen whales that produce sounds in the 10 - 2000 Hz frequency range. When fitted with onboard, automated detection and identification algorithms, the gliders can become powerful tools for near real-time monitoring of the at-risk whales and thus risk mitigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".