Effects of anthropogenic noise on haul-out numbers of harbor seals (<i>Phoca vitulina)</i>
Bibliographic record
Abstract
Haul-out sites of harbor seals, Phoca vitulina Linnaeus, 1758, include areas with high levels of anthropogenic noise. In some cases, seals haul out at night when there are lower in-air noise levels. However, it is unclear whether there are additional responses to noise pollution. To determine potential impacts of anthropogenic noise on haul-out behavior, we compared numbers of hauled-out harbor seals relative to in-air noise levels at two sites in Washington state, USA—one close to human activities (Bellingham waterfront) and one more distant (Semiahmoo marina)—between July 2020 and August 2021. We used generalized linear mixed models to identify predictors of seal numbers. The marina had lower mean noise levels than the waterfront (39.7 ± SD 6.1 dB, n = 29 observations versus 51.2 ± SD 5.2 dB, n = 126 observations). The plotted model prediction showed a significantly negative association between noise and seals at the marina, and no association was found at the waterfront. Results indicate that in-air noise levels may influence seal numbers at sites where human activities are low. They also suggest that, besides hauling out at night, seals may become tolerant to in-air noise levels at sites where human activities are high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".