Exposure and behavioural responses of tagged bowhead whales (<i>Balaena mysticetus</i>) to vessels in the Pacific Arctic
Bibliographic record
Abstract
Arctic marine mammals face many challenges linked to climate change, including increasing anthropogenic noise from vessel traffic. The bowhead whale ( Balaena mysticetus Linnaeus, 1758), an Arctic endemic cetacean, relies on acoustic communication, with documented overlapping frequencies between communication and vessel noise. Bering–Chukchi–Beaufort (BCB) bowhead whales migrate through areas with the highest levels of vessel traffic in the Pacific Arctic. Here, we document the spatial and temporal overlap between 25 satellite-tagged BCB bowhead whales and vessels during July–December, 2012–2018. We report 1332 occasions when a vessel was within 125 km of a tagged whale, and where possible, quantified changes in swim speed to investigate individual behavioural responses to vessel approaches within a 50 km radius ( n = 18 encounters). In the quantitative analysis, bowhead whales were not observed to alter swim speed within 8–50 km of vessels (we could not assess distances <8 km). Our results suggest that bowhead whales did not exhibit detectable long-range (i.e., up to 50 km) behavioural responses to vessels, consistent with observations of closely related North Atlantic right whales ( Eubalaena glacialis (Muller, 1776)), for which vessel strikes are a leading cause of mortality. More work is required to assess how bowhead whales react to vessels at closer distances.
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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 | 0.001 |
| 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".