Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales
Bibliographic record
Abstract
Successful area-based ocean management relies on long-term, persistent biological monitoring using reliable ocean observation assets. Underwater electric gliders fill a unique monitoring niche compared to other platforms because they can autonomously survey across diverse environments—from shallow coastal waters to remote offshore areas—for weeks to months at a time. Gliders equipped with passive acoustic monitoring (PAM) devices are capable of robust, continuous near-real-time monitoring of numerous species of whales. Here, we highlight five case studies to discuss how gliders are being used for area-based monitoring of the internationally migratory and critically endangered North Atlantic right whale to address several different spatial management objectives. Examples include dynamic management of shipping zones and fishery-area closures in Canadian waters, glider-based monitoring in the United States to mitigate vessel strikes and fishing gear entanglements, surveys to assess whale habitat use near offshore wind energy development areas in the northeastern United States, and surveillance of the coastal calving grounds in the southeastern United States. These examples illustrate how PAM-equipped gliders are being used to monitor an endangered cetacean species with complex conservation management needs across its range. These assets are supporting risk reduction measures across diverse regions, and their use is likely to continue to expand in support of species conservation and threat 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 | 0.001 | 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 | 0.001 |
| 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".