Estimating North Atlantic right whale ( <scp> <i>Eubalaena glacialis</i> </scp> ) location uncertainty following visual or acoustic detection to inform dynamic management
Bibliographic record
Abstract
Abstract The United States and Canada employ dynamic management strategies to improve conservation outcomes for the endangered North Atlantic right whale ( Eubalaena glacialis ). These strategies rely on near real‐time knowledge of whale distribution generated from visual surveys and opportunistic sightings. Near real‐time passive acoustic monitoring (PAM) systems have been operational for many years but acoustic detections of right whales have yet to be incorporated in dynamic management because of concerns over uncertainty in the location of acoustically detected whales. This rationale does not consider whale movement or its contribution to location uncertainty following either visual or acoustic detection. The goal of this study was to estimate uncertainties in right whale location following acoustic and visual detection and identify the timescale at which the uncertainties become similar owing to post‐detection whale movement. We simulated whale movement using an autocorrelated random walk model parameterized to approximate three common right whale behavioral states (traveling, feeding, and socializing). We then used a Monte Carlo approach to estimate whale location over a 96‐hr period given the initial uncertainty from the acoustic and visual detection methods and the evolving uncertainties arising from whale movement. The results demonstrated that for both detection methods the uncertainty in whale location increases rapidly following the initial detection and can vary by an order of magnitude after 96 hr depending on the behavioral state of the whale. The uncertainties in whale location became equivalent between visual and acoustic detections within 24–48 hr depending on whale behavior and acoustic detection range parameterization. These results imply that using both visual and acoustic detections provides enhanced information for the dynamic management of this visually and acoustically cryptic and highly mobile species.
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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".