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Record W2767593871 · doi:10.1139/cjfas-2017-0245

Integrating techniques: a review of the effects of anthropogenic noise on freshwater fish

2017· review· en· W2767593871 on OpenAlexaffvenue
Megan F. Mickle, Dennis M. Higgs

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFreshwater fishNoise (video)Environmental scienceFish <Actinopterygii>FisheryEcologyStressorDiversity of fishMarine speciesBiologyComputer science

Abstract

fetched live from OpenAlex

In recent years, the effects of anthropogenic noise on freshwater fish has been of increasing interest for fishery managers due to rising levels of this background noise. While it is clear that anthropogenic noise can have important impacts on mammals and marine fish, much less is known about these effects in fresh water. The influence of anthropogenic noise on freshwater fish can be quantified using the same methods as with marine species — through measuring changes in behavioural and physiological outputs. Here, we briefly review the literature regarding behavioural and physiological impacts of noise pollution on freshwater fish and further note the lack of incorporation of both behavioural and physiological measures within current studies. We call for an increased research emphasis on possible effects of anthropogenic noise on freshwater fish and further suggest that the integration of behavioural and physiological techniques is critical for a full understanding of these effects. While freshwater fish face many stressors, it is unclear how important anthropogenic noise really is, and this issue can only be properly resolved through careful study.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.294
Teacher spread0.254 · 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 designOther design
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

Citations88
Published2017
Admission routes2
Has abstractyes

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