The impact of in-air noise on the behavior of harbor seals ( <i>Phoca vitulina</i> ) at the water’s surface
Bibliographic record
Abstract
Pollution, such as in-air noise, is a threat to animals whose habitat heavily overlaps with areas of human development. The sensitive in-air hearing of harbor seals ( Phoca vitulina Linnaeus, 1758), a pinniped species, makes them particularly susceptible to anthropogenic noise. Hauled-out harbor seals alter their behavior in response to in-air noise. Yet, to our knowledge there has been no effort to establish whether a similar pattern occurs while seals are in the water with their head above the surface. To address this gap, we compared the number of surfaced seals and the duration of time they spent at the surface (surfacing duration) to in-air noise at the Bellingham Waterfront in Washington State, USA. We used generalized linear mixed models with a Bayesian framework to determine whether in-air noise predicted the number of seals and their surfacing durations. We found no relationship between in-air noise levels and the numbers of seals present at the waterfront or the surfacing durations. This suggests that the seals who frequent this site tolerate the local in-air noise levels. Given that the site is heavily used by humans, future work should examine if this tolerance puts seals at risk of harmful interactions with human activities.
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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".