Derivations of transfer functions for estimating ship underwater radiated noise from onboard vibrations
Bibliographic record
Abstract
The adverse impacts of underwater radiated noise (URN) from marine vessels on marine life are increasingly recognized. URN estimation and subsequent monitoring could be used to track URN and take measures to reduce it in sensitive environments (e.g., a marine life protected area). Two common analytical radiated noise power approximations and two empirical methods are assessed for URN estimation. The assessments were carried out by testing the methods on data from acoustic range measurements of an ORCA-class training vessel of the Royal Canadian Navy, named Patrol Craft Training (PCT) Moose and a DAMEN Combi Freighter 3850, named Heinz G. The analytical approximations are called equivalent radiated power and power from volume velocity; both are used to estimate URN directly from the onboard ship vibration measurements. The empirical methods are based on the correlations of onboard vibrations with measured URN, from which the derived transfer functions. The analytical and empirical transfer functions are compared. A statistical energy analysis model of PCT Moose is also used to estimate onboard vibrations from the ship’s engine, generator, and propeller specifications for URN estimation at the design stage.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".