Vessel traffic disrupts walrus vocal behavior in a proposed marine protected area
Bibliographic record
Abstract
Vessel traffic and underwater noise pollution are increasing in the Arctic. Marine mammals are sensitive to underwater noise from vessels which can negatively impact them at the individual and population levels. The marine region of Southampton Island, Nunavut, Canada, is a recognized key area for many marine mammal species and is under consideration to become a marine protected area. Given the increase in vessel traffic in the region, this study explores the potential impact of vessel traffic noise on the vocal behavior of walruses and belugas. This represents the first study to investigate walrus vocal behavior during exposure to vessels. Underwater acoustic data were collected near Southampton Island from June to November 2018. Vessel movements were tracked using the Automatic Identification System (AIS) data and compared with underwater recordings to identify noise sources by vessel type (ship or motorboat). Generalized linear mixed models were used to assess changes in walrus vocalization rates before, during, and after vessel encounters across vessel type. The results showed that walrus vocalization rates decreased during and after vessel encounters and were significantly lower in the presence of ships than motorboats. Belugas were never recorded during motorboat transits, which may indicate avoidance behavior. However, there was not enough data to investigate this hypothesis further. Our findings demonstrate that vessel traffic influences walrus vocal behavior and highlight the need for updated maritime navigation mitigation measures in the study area.
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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".