Assessing potential perception of shipping noise by marine mammals in an arctic inlet
Bibliographic record
Abstract
Shipping is increasing in Arctic regions, exposing marine mammals to increased underwater noise. Noise analyses often use unweighted broadband sound pressure levels (SPL) to assess noise impacts, but this does not account for the animals' hearing abilities at different frequencies. In 2018 and 2019, noise levels were recorded at five and three sites, respectively, along a shipping route in an inlet of Northern Baffin Island, Canada. Broadband SPLs (10 Hz-25 kHz), unweighted and with auditory weighing functions from three marine mammal groups, were compared between times ore carriers (travelling < 9 knots) were present or absent. Clearly audible distances of shipping noise and exposure durations were estimated for each weighting function relative to vessel direction, orientation, and year. Auditory weighting functions had significant effects on the potential perception of shipping noise. Bowhead whales (Balaena mysticetus) experienced similar SPLs to unweighted levels. Narwhals (Monodon monoceros) and ringed seals (Pusa hispida) experienced lower SPLs. Narwhals were unlikely to clearly perceive shipping noise unless ships were in close proximity (<3 km) and ambient noise levels were low. Detectability propagation models of presumed noise exposure from shipping must be based on the hearing sensitivities of each species group when assessing noise impacts on marine mammals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".