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Record W4412812564 · doi:10.3397/nc_2025_0062

The commercial era of vessel underwater radiated noise: past, present, and future

2025· article· en· W4412812564 on OpenAlexaboutno aff
Michael Bahtiarian

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsUnderwaterNoise (video)AcousticsTelecommunicationsEnvironmental scienceEngineeringComputer scienceHistoryPhysicsArchaeologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.246
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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