Whales on the marine highway: assessing the risk of ship strikes to humpback (Megaptera novaeangliae) and fin (Balaenoptera physalus) whales off the west coast of Vancouver Island, British Columbia, Canada
Bibliographic record
Abstract
Vessel strikes are a source of mortality and injury for baleen whales, particularly near shipping lanes, that can have population-level impacts. Quantifying mortality from this threat is a challenge because carcasses sink. Methods to estimate risk of collision using whale distributions data and marine traffic data in a spatial analysis are useful to identify hotspots of risk. To assess ship strike risk to whales, we conducted 34 systematic aerial surveys (2012-2015) to estimate humpback and fin whale distribution and relative density off the west coast of Vancouver Island, Canada including approaches to Juan de Fuca Strait, a shipping gateway to several major west-coast ports in the Salish Sea. We fit sightings (330 humpback and 120 fin whales) and effort data from our surveys to Generalized Additive Models (GAMs) to predict whale densities over a gridded surface over the study area. Humpbacks were associated with the continental shelf, with highest densities along the shelf edge (~400 m), whereas fin whales largely occurred west of the shelf in deeper water (>400 m). We mapped shipping intensity data from 2013 on the same gridded surface, and compared shipping intensity to model-predicted whale densities to estimate relative risk of vessel strikes throughout the study area. Since vessel speed is an important determinant of collision lethality, we also calculated the relative risk of lethal injuries as a result of ship speed per grid cell. Results serve to focus further research in this region and lead to opportunities to discuss conservation and management options to reduce this threat and thereby support recovery of endangered whale 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".