Quantification, characterization and attenuation of the acoustic footprint of the maritime transport in the St. Lawrence seaway - The MARS observatory
Bibliographic record
Abstract
Marine traffic is the main contributor to ocean noise at low frequencies, contributing to an observed increase of up to 10 dB over the few decades. It is predicted to further intensify over the coming decades and expand dramatically in the Arctic following new routes made accessible by global warming and sea-ice melt. Anthropogenic noise has a demonstrated impact on marine environment, through masking of intraspecific and interspecific communication, affecting predator-prey interactions hampering settlement cues and reducing threat (including vessel) detection, resulting in increased stress levels and reduction of habitat suitability. The St. Lawrence Estuary (eastern Canada) is a major shipping corridor linking the Great Lakes to the Atlantic Ocean, and home to 13 marine mammal species including Endangered Beluga whale and blue whale and Critically Endangered North Atlantic right whale. Understanding and reducing the acoustic footprint of maritime transport is critical to improve its cohabitation with the marine fauna.The Marine Acoustic Research Station (MARS, www.projet/mars.ca/enwww.projet-mars.ca/en) is an applied research project focused on quantifying, understanding and attenuating traffic noise and its effects on marine life. An acoustic recording station was specifically designed to collect high-resolution measurements of the source levels radiated by a significant part of the commercial fleet operating in the St. Lawrence Seaway. The MARS observatory contributes to improving knowledge of the underwater noise emissions in the St. Lawrence Estuary through quantitative measurements, comprehensive analysis and modeling of ship noise. From our high-resolution source level database, we developed a source level model, tailored to the St. Lawrence fleet, to further understand, model and predict the acoustic footprint of traffic noise in the region, critical to conservation actions and traffic management.Over the first three years of operation, we collected over 2500 source level measurements. A suite of autonomous onboard vibration sensors has been developed and deployed on 15 ships for comprehensive characterization onboard vibrations. Our observations provide quantitative information about the underwater noise generated by the St. Lawrence fleet to the government, for informed decisions regarding the establishment of source level limits. We also deliver quantitative feedback to shipowners regarding the contribution of each of their vessels to underwater noise, and we help raising awareness within their teams and identifying suitable attenuation solutions. Finally, the MARS team contributes to improve ship usage, modification and design to reduce noise emissions, through characterization of the mechanical processes, onboard the ships, related to noise generation, transmission, and radiation in the ocean.The long-term operation of the MARS observatory offers a unique framework to advance scientific and technical knowledge regarding ship noise, and a robust measurement infrastructure to test and quantify the effects of noise mitigation solutions for individual vessels through successive measurements, as well as to monitor the fleet-wide effects of traffic management decisions and technology development over the years on the acoustic footprint of maritime transport.
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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 | 0.000 | 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".