Underwater noise characterization of a typical fishing vessel from Atlantic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it