Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales
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
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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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