Exposure and behavioral responses of tagged beluga whales (<i>Delphinapterus leucas</i>) to ships in the Pacific Arctic
Bibliographic record
Abstract
Abstract Arctic marine mammals face a multitude of challenges linked to climate change, including increasing anthropogenic noise from ship traffic. The beluga whale (Delphinapterus leucas), a predominately Arctic endemic cetacean, relies heavily on acoustic communication, with documented overlap between their vocalizations and hearing range and ship noise. Some belugas migrate through areas with the highest levels of ship traffic in the Pacific Arctic and exposure to ship noise is highly probable. Here, we document the responses of nine satellite‐tagged Eastern Beaufort Sea belugas to encounters with ships in the Beaufort, Chukchi, and Bering Seas during July–December 2018. We report 177 occasions when ships were within 125 km of tagged belugas and quantified changes in lateral and vertical movements to investigate individual behavioral responses to ship approaches within 50 km (n = 23). Belugas' swim speed was negatively correlated with ship distance, showing possible changes in swim speed up to 79 km away. Changes in lateral and vertical movements, indicating disruption of behavior, were observed when some ships passed within 50 km. These findings corroborate previous studies that have shown behavioral responses of belugas to ships at distances far beyond visual range, implying belugas react to low‐amplitude ship noise near ambient levels.
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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 | 0.000 |
| 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".