UNDERWATER RADIATED NOISE FROM A LARGE PLEASURE CRAFT
Bibliographic record
Abstract
The impact of underwater radiated noise (URN) by ship traffic has gained an increasing interest among scientists, ship designers and builders. It is now recognized that the underwater noise generated by human activities and in particular shipping noise can be harmful for the marine fauna and therefore urgent actions must be taken to tackle the problem. To this aim a virtuous example is represented by the Port Authority of Vancouver (CA) that, since 2017, has introduced important incentives and tax relief for those ships that prove to be particularly virtuous in terms of noise emissions radiated into the water. Up to now, most of the attention has been paid to study and characterize the noise emissions of large commercial ships due their worldwide diffusion. To this aim several measurements protocols have been issued both by international bodies (ISO, ANSI/ASA) both by the main classification societies. A lack of data is on the contrary present regarding pleasure crafts both as regards small boast and large yachts. In the present paper data coming from an experimental campaign for the measurement of the underwater radiated noise of one large yacht built by the SanLorenzo shipyards is presented. Noise has been measured for several operative conditions and speeds ranging from zero to maximum speed.
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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) | 0.001 | 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".