Characterization, understanding, and mitigation of underwater noise radiated by ships in the St. Lawrence Estuary
Bibliographic record
Abstract
Underwater noise generated by commercial traffic is the main source of anthropogenic noise pollution at low frequencies, increasingly present at a global scale and of critical interest in the St. Lawrence Estuary (eastern Canada), where a rich biodiversity meets the shipping corridor linking the Great Lakes to the Atlantic Ocean. The Marine Acoustic Research Station (MARS, www.projet-mars.ca/en) is an applied research project dedicated to characterizing, understanding, and mitigating underwater traffic noise, contributing to the global effort of improving cohabitation between human activities and marine life. A cutting edge marine acoustic observatory has been specifically designed to collect high-quality measurements of the underwater noise radiated by ships. It is deployed yearly since 2021, operating during the ice-free season. A database of over 2000 measurements representative of the underwater noise radiated by the fleet operating in the St Lawrence Estuary has been collected, with a demonstrated repeatability of less than 1.5 dB which confirms the ability of the observatory to effectively assess the efficiency of noise reduction measures. The MARS database supports the development of a noise prediction model and provides feedback to shipowners and relevant information regarding the St. Lawrence fleet to the government for future underwater vessel noise reduction targets.
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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) | 0.000 | 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".