Comparison of visual and acoustic surveys for the detection and dynamic management of North Atlantic right whales ( <scp> <i>Eubalaena glacialis</i> </scp> ) in Canada
Bibliographic record
Abstract
Abstract The goal of this study was to develop a simulation to quantitatively compare acoustic and visual surveys and use it to inform current and future North Atlantic right whale ( Eubalaena glacialis ) risk mitigation. We expanded upon an established whale movement model, incorporating realistic right whale cues for visual and acoustic detection within dynamic management zones in the Gulf of Saint Lawrence, Canada. Survey transits by acoustic (Slocum gliders) and visual (aircraft, vessels, and Remotely Piloted Aircraft Systems) platforms were simulated using representative platform movements and detection functions. We used a Monte Carlo approach to estimate the probability of detecting a cue, in each zone, as a function of survey platform, number of right whales, and survey transits. Acoustic gliders detected right whale presence in every scenario. Single transits of a management zone by visual surveys were only able to reliably (>0.5 probability) detect right whales when more than 20 whales were present. Twenty or more transits were required to reliably detect a single right whale. Our results serve as a tool to be used by decision‐makers to inform optimal right whale monitoring strategies that consider the relative strengths of the various platforms.
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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 | 0.003 |
| 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) | 0.001 | 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".