Underwater noise characterization of a typical fishing vessel from Atlantic Canada
Bibliographic record
Abstract
Fishing is a significant economic sector in Atlantic Canada. Increased fishing vessel density raises ocean ambient noise , adversely affecting the marine ecosystem. This study aimed to evaluate the underwater radiated noise emitted by a typical fishing vessel, identify the noise sources, and determine their respective contributions to the overall noise signature of the vessel. Dipole and monopole source levels are estimated through passive acoustic monitoring during sailing and stationary conditions. This is accomplished by employing a set of hydrophones in conjunction with a numerical propagation loss model and oceanography data. This study also addresses the correlation between structure-borne noise and underwater radiated noise. Over 70% of the noise at frequency bands of 63 and 250 Hz and approximately 40% of the noise above 1 kHz were attributed to the diesel engine , indicating that it significantly contributed to the vessel’s noise signature with the propeller. In the unclutched propeller mode, the vessel engine continues to generate noise 40 dB above the background noise and peaks at the 250 Hz band. By addressing this knowledge gap, we can potentially contribute to future endeavours to enhance fishing vessel design and lower the impact of individual noise sources.
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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 | 0.000 | 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".