VESSEL COLLISIONS WITH WHALES: THE PROBABILITY OF LETHAL INJURY BASED ON VESSEL SPEED
Bibliographic record
Abstract
Abstract Historical records demonstrate that the most numerous, per capita, ocean‐going‐vessel strikes recorded among large‐whale species accrue to the North Atlantic right whale ( Eubalaena glacialis ). As vessel speed restrictions are being considered to reduce the likelihood and severity of vessel collisions with right whales, we present an analysis of the published historical records of vessels striking large whales. We examine the influence of vessel speed in contributing to either a lethal injury (defined as killed or severely injured) or a nonlethal injury (defined as minor or no apparent injury) to a large whale when struck. A logistic regression model fitted to the observations, and consistent with a bootstrap model, demonstrates that the greatest rate of change in the probability of a lethal injury ( P lethal ) to a large whale occurs between vessel speeds of 8.6 and 15 knots where P lethal increases from 0.21 to 0.79. The probability of a lethal injury drops below 0.5 at 11.8 knots. Above 15 knots, P lethal asymptotically approaches 1. The uncertainties in the logistic regression estimates are relatively large at relatively low speeds ( e.g. , at 8 knots the probability is 0.17 with a 95% CI of 0.03–0.6). The results we provide can be used to assess the utility of vessel speed limits that are being considered to reduce the lethality of vessels striking the critically endangered North Atlantic right whale and other large whales that are frequent victims of vessel strikes.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".