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Record W4403268053 · doi:10.3397/in_2024_2156

Laboratory measurement of the effect of exposure to ship noise on mussels

2024· article· en· W4403268053 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNOISE-CON proceedings · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersXunta de Galicia
KeywordsNoise (video)Environmental scienceFisheryMarine engineeringAcousticsEngineeringBiologyComputer sciencePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The mussel Mytilus galloprovincialis is a bivalve mollusc of high commercial interest, which frequently breeds in coastal areas with high levels of ship traffic noise. Studies on the effect of this stressor on mussels are scarce, which is why we set out to design a system to study the effect of ship noise on mussel behaviour (valve opening-closure rhythms). The proposed experimental set-up uses a high frequency non-invasive valvometry system to detect shell closure reactions related to instantaneous increases in sound level. The main findings are that mussels reacted to ship noise levels above 114 dB re 1μPa (63Hz-4kHz), that most reactions occurred within 13 s after a sudden increase in level, and that there does not appear to be a clear relationship between closure depth and noise level. Although mussels may not be sensitive to sound pressure but only to the associated particle movement, the results of this work contribute to highlighting the sensitivity of this species to noise, and the need to carry out experiments with a larger number of specimens and in conditions that allow the acoustic field to be reproduced more realistically.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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