A methodology to define underwater acoustic radiated noise norms for small commercial vessel classes using neural networks
Bibliographic record
Abstract
To address the International Maritime Organization's underwater radiated noise (URN) reduction guidelines (MEPC.1/Circ.833), vessels undertake rangings to evaluate their URN against classification society norms. However, norms for smaller vessels, like Canada's inshore fishing fleet, are not established. The proposed methodology informs URN class norms for small commercial vessels, while minimizing individual vessel rangings, through three steps. Firstly, representative class members are acoustically ranged over the range of operating conditions. URN is logged and correlated with on-board measures of structure-borne noise, ma- chinery states, hull fouling, and weather. Secondly, a neural network (NN) is trained to predict the URN from these logged measures. Third and finally, the network's sensitivity to logged measures is analyzed using per-mutation importance and dropout. Sensitive features demarcate the class, or must be logged for each vessel, towards accurate predictions. This methodology is demonstrated on the Cape Islanders class. The trained NN predicted the decidecade URN spectrum (100Hz - 50kHz) to an accuracy of 6.6dB re: 1microPascal at 1m. URN prediction sensitivity to in-water conditions, engine speed, engine power, cavitation-induced hull vibrations, and hull fouling extent indicates important features to log. This shows that class-wide analysis of URN can inform small vessel class norms using the proposed methodology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".