Soundscapes experienced by southern resident killer whales (<i>Orcinus orca</i>) in the Salish Sea and implications of vessel noise
Bibliographic record
Abstract
Endangered southern resident killer whales (SRKW, Orcinus orca) use waters of the Salish Sea around southern British Columbia and northern Washington State to forage, following the in- migration of prey. However, these waterways experience heavy vessel traffic, with important SRKW foraging areas near to international shipping lanes. Here we use recordings from six passive acoustic moorings to characterize the soundscape, and determine spatiotemporal patterns on daily to annual scales. Particular focus is given to the noise levels in frequencies used by SRKW to communicate and echolocate. A numerical model is used to map the sound field between the mooring locations and examine the anthropogenic inputs in SRKW critical habitat in the Salish Sea by commercial traffic. Extrapolation of the model output to higher frequencies allows an examination of the impact this may have on the range of effective acoustics use, with implications for SRKW navigation, foraging success and group cohesion. Finer scale analysis of vessel signatures as they transit the study area aids in identifying areas of increased risk of disturbance for whales. In addition, differences in modelled to observed data will help the characterisation of other sonic inputs to the soundscape, including smaller recreational vessels.
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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".