Probability and mitigation of vessel encounters with North Atlantic right whales
Bibliographic record
Abstract
Successful mitigation of vessel-whale encounters requires quantitative estimates of vessel strikes, how strike rates change over time, where strikes are most likely to occur, and options for minimizing strikes. In addressing these issues, we first demonstrate a 3- to 4-fold increase in the number of reported large whale-vessel strikes worldwide from the early 1970s to the early 2000s, corresponding to a 3-fold increase in the number of vessels in the world fleet that is paralleled by an increase in vessel tonnage and speed. Second, we estimate a 50% chance of 14 or more annual vessel-strike reports worldwide between 1999 and 2002. For North Atlantic right whales Eubalaena glacialis, we estimate a 60% chance of observing at least 1 right whale death from vessel strike. Adjusting for undetermined causes of death and unobserved deaths, we estimate a 10-fold increase (from 1 to 10) in the expected annual number of fatal ship strikes. Third, we evaluate the eastern United States geographic distribution of right whales and vessels to calculate relative probabilities of vessel-whale encounters among 3 major right whale habitats. We determine that the Southern Calving Ground poses the greatest threat of a vessel strike: 1.6- and 7-fold greater than in Cape Cod Bay and the Great South Channel, respectively. Finally, for the Great South Channel region we present a quantitatively determined vessel-traffic routing option that would achieve a 39% reduction in vessel-whale encounter probabilities. The methods employed in assessing encounter probabilities and vessel-routing options can be applied elsewhere to enhance the conservation of endangered and threatened species that suffer vessel-strike mortality.
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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 | 0.006 |
| 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) | 0.002 | 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".