The commercial era of vessel underwater radiated noise: past, present, and future
Bibliographic record
Abstract
Today Underwater Radiated Noise (or URN) is not just about keeping submarines and combatant surface vessels undetectable. Since the mid 1990’s, vessel URN moved into the public domain for many purposes. Approaching 30 years as an open topic, much has been learned, many new standards have been issued, and many more are being developed. As an example, the standard for general acoustic terms, ANSI S1.1 was first published in 1960 with roots back to 1942. Whereas the standard for general underwater acoustic terms, ISO-18405 was just published in 2017, as much as 75 years later. Methodologies for measurement of URN have been codified by ANSI, ISO, and ship classification societies. Europe has written directives for URN limits; Canada is in the process of similar limits and U.S. environmentalists would like to follow. The International Maritime Organization (IMO, a UN subsidiary) has significant guidelines on vessel URN. This paper will highlight where and when URN became commercially relevant. The current metrics, methods, and other guidelines for measurement of ship source sound levels will be addressed. Finally, what will the future bring for shipboard URN limitations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".