Avoiding sharp accelerations can mitigate the impacts of a Ferry’s radiated noise on the St. Lawrence whales
Bibliographic record
Abstract
Exposure to anthropogenic noise from the commercial fleet is one of the primary constituents of the acoustic pollution perturbing the environment of aquatic life. Merchant ships (e.g. bulkers, tankers) have been the focus of numerous studies for underwater noise source level determination and modeling. This work extends pre-existing studies to the ferry ship class. Hydrophone-based measurements of the N.M. Trans-Saint-Laurent ferry near the Rivière-du-Loup harbor (Rivière-du-Loup, QC CANADA) were obtained for 186 transits between 2020 July 22th and 2020 September 5th. For each transit, monopole source levels are estimated for two (2) different modes of operation i.e., the low-speed phases of acceleration/deceleration when the ferry launches/docks at Rivière-du-Loup and the passages at quasi-operational speed at the hydrophone's closest-point-of-approach. Relative differences between the two (2) modes of operation are presented here in the low-frequency domain between 141 and 707 Hz. An average excess of 8 to 11.5 dB indicates that the ferry is likely one order of magnitude noisier, within this frequency band, during acceleration/deceleration when compared to passages at operational speed. This highlights that, in terms of marine mammal conservation, a significant reduction of the noise pollution could be achieved, for instance, by avoiding sudden speed changes in the vicinity of whales.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".