MétaCan
Menu
Back to cohort
Record W4413346361 · doi:10.1121/10.0038636

A call for comparable measurements of underwater radiated noise related to vessel speed reduction programs

2025· article· en· W4413346361 on OpenAlexafffund
Leila Hatch, Megan F. McKenna, Rianna E. Burnham, Kaitlin E. Frasier, Christine M. Gabriele, Sean Hastings, Samara M. Haver, Anastasia Kunz, Alexander O. MacGillivray, Chloë Malinka, Jessica Morten, Lindsey Peavey Reeves, Krista Trounce, Svein Vagle, Jason Wood, Vanessa M. ZoBell

Bibliographic record

VenueJASA Express Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsGovernment of CanadaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaOffice of National Marine SanctuariesTransport Canada
KeywordsUnderwaterComparabilityNoise (video)Reduction (mathematics)Computer scienceNoise reductionMarine engineeringEnvironmental scienceEngineeringArtificial intelligenceMathematicsGeologyOceanography

Abstract

fetched live from OpenAlex

Marine vessels are mandated or requested to reduce speed to meet operational, economic, and conservation goals. Vessel speed reduction (VSR) is a key strategy in global efforts to reduce ocean noise. Many regional VSR programs incorporate underwater acoustic monitoring to assess reductions in underwater radiated noise (URN) from vessels. Drawing from North American VSR programs, approaches to measuring URN reduction are assessed and progress toward robust metrics, scalable methods, and integrative measures are documented. Improved alignment across programs is recommended across programs to achieve measurement comparability to advance VSR evaluation and contribute to global underwater noise reduction and sustainable shipping goals.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.605

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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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 designBench or experimental
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 routes2
Has abstractyes

Explore more

Same venueJASA Express LettersSame topicMarine animal studies overviewFrench-language works237,207