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Record W4388422800 · doi:10.1121/10.0022254

Navigating noisy waters: A review of field studies examining anthropogenic noise effects on wild fish

2023· review· en· W4388422800 on OpenAlexafffund
Rachel H. Pieniazek, R. K. Beach, Grace M. Dycha, Megan F. Mickle, Dennis M. Higgs

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaGenome Canada
KeywordsFish <Actinopterygii>Noise (video)Field (mathematics)Environmental scienceVariety (cybernetics)Aquatic ecosystemComputer scienceEnvironmental resource managementEcologyFisheryBiologyMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Anthropogenic noise is globally increasing in aquatic ecosystems, and there is concern that it may have adverse consequences in many fish species, yet the effects of noise in field settings are not well understood. Concern over the applicability of laboratory-conducted bioacoustic experiments has led to a call for, and a recent increase in, field-based studies, but the results have been mixed, perhaps due to the wide variety of techniques used and species studied. Previous reviews have explored the behavioral, physiological, and/or anatomical costs of fish exposed to anthropogenic noise, but few, if any, have focused on the field techniques and sound sources themselves. This review, therefore, aims to summarize, quantify, and interpret field-based literature, highlight novel approaches, and provide recommendations for future research into the effects of noise on fish.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations18
Published2023
Admission routes2
Has abstractyes

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