A threatened beluga (<i>Delphinapterus leucas</i>) population in the traffic lane: Vessel-generated noise characteristics of the Saguenay-St. Lawrence Marine Park, Canada
Bibliographic record
Abstract
The threatened resident beluga population of the St. Lawrence Estuary shares the Saguenay-St. Lawrence Marine Park with significant anthropogenic noise sources, including marine commercial traffic and a well-established, vessel-based whale-watching industry. Frequency-dependent (FD) weighting was used to approximate beluga hearing sensitivity to determine how noise exposure varied in time and space at six sites of high beluga summer residency. The relative contribution of each source to acoustic habitat degradation was estimated by measuring noise levels throughout the summer and noise signatures of typical vessel classes with respect to traffic volume and sound propagation characteristics. Rigid-hulled inflatable boats were the dominant noise source with respect to estimated beluga hearing sensitivity in the studied habitats due to their high occurrence and proximity, high correlation with site-specific FD-weighted sound levels, and the dominance of mid-frequencies (0.3-23 kHz) in their noise signatures. Median C-weighted sound pressure level (SPL(RMS)) had a range of 19 dB re 1 μPa between the noisiest and quietest sites. Broadband SPL(RMS) exceeded 120 dB re 1 μPa 8-32% of the time depending on the site. Impacts of these noise levels on St. Lawrence beluga will depend on exposure recurrence and individual responsiveness.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".