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Record W2560525854 · doi:10.1121/2.0000291

Assessing the effect of aquatic noise on fish behavior and physiology: a meta-analysis approach

2016· article· en· W2560525854 on OpenAlexafffund
Kieran Cox, Lawrence P. Brennan, Sarah E. Dudas, Francis Juanes

Bibliographic record

VenueProceedings of meetings on acoustics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsVancouver Island UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsSoundscapeHabitatFish <Actinopterygii>PredationNoise (video)Marine speciesEcologyMarine habitatsBiologyEnvironmental scienceFisheryComputer scienceSound (geography)Oceanography

Abstract

fetched live from OpenAlex

Due to the extreme distance that sounds can travel through water, many marine species rely on the soundscape for auditory information regarding predator or prey locations, communication, and habitat selection. These species not only take advantage of the prevailing sounds but also contribute to the soundscape through their own vocalizations. Certain sounds have been shown to have negative effects on marine species, resulting in disrupted communication and unbalanced predator-prey interactions. Unfortunately, the vast majority of soundscape studies are biased towards marine mammals, and only recently has attention been directed towards the potential repercussions for fishes. In an attempt to determine the implications that changes to the soundscape may have on the fishes, a meta-analysis was conducted focusing primarily on the role that anthropogenic noises may play in altering fish behavior and physiology. The review identified 3,174 potentially relevant papers of which were 27 used. The analysis indicates that anthropogenic noise has an adverse effect on marine and freshwater fish behavior and physiology. These findings suggest that although certain species may be more susceptible to anthropogenic noise than others, the vast majority of fish have the potential to be negatively affected by noise pollution.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.491
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.282
Teacher spread0.249 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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