Evaluation of the performance of technologies for reducing ships' machinery noise using a small-scale ship-like structure in a Water Basin
Bibliographic record
Abstract
Ocean ambient levels have increased in the last decades, especially in the low-frequency domain (under 500 Hz). This increase is partly due to underwater radiated noise (URN) from commercial ships. Excessive URN harms marine life and is, therefore, considered a pollution that needs to be reduced. At low speeds, machinery is the primary noise source on ships. Mitigation technologies exist to limit machinery’s contribution to URN. While implementing these technologies is costly, a lack of quantitative data regarding their exact performances usually results in limited concrete ship applications since the cost-to-benefit ratio is imprecise. This study aims to quantify better the performance of standard noise mitigation technologies using a small-scale ship-like structure in a water basin. The basin’s acoustic field is first characterized with and without the structure. The structure is then equipped with different mitigation technologies. A loudspeaker and a vibration shaker are fed with pink noise or measured signals on actual machinery. Hydrophones, microphones, accelerometers, and force sensors measure the response in the basin and on the structure. The performance of each tested technology is evaluated and ranked in terms of URN reduction. The relative contributions of airborne and structure-borne transmission paths on URN are also examined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".