Underwater radiated noise explained from ship characteristics and operating conditions - Model obtained from the MARS database
Bibliographic record
Abstract
Understanding how ship characteristics and operating conditions impact underwater radiated noise (URN) is key to identifying essential avenues for noise reduction and predict associated risks for the marine life. Previous studies have revealed that current models could only explain up to 50% of the observed variability in URN levels. In this study, we used a unique dataset, collected within the Marine Acoustic Research Station (MARS) project (www.projet-mars. ca/en). The dataset is consisting of ~1000 acoustic signatures from vessels in the St. Lawrence shipping lane (eastern Canada), and 174 high-quality signatures from partner vessels following an optimized measurement protocol and considering design parameters, meteorological, and oceanographic data. Applying functional regression, as described by MacGillivray et al. (2022), we quantified the relationship between vessel characteristics, operation conditions and URN. Our findings quantify the direct effect of the speed, size, and draft on URN across a frequency range of 10-500 Hz. Using these relationships, we propose a tailored URN predictive model representative of the St. Lawrence fleet. We then compare its performance to previously published models and asses the gains linked to the active collaboration of ship owners, contributing to the wide range of available operation conditions available in our dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".