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Record W3013887194 · doi:10.1121/2.0001217

Impacts of noise on the behavior and physiology of marine invertebrates: A meta-analysis

2019· article· en· W3013887194 on OpenAlexafffund
Kelsie A. Murchy, Hailey L. Davies, Hailey Shafer, Kieran Cox, Kat Nikolich, Francis Juanes

Bibliographic record

VenueProceedings of meetings on acoustics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Victoria
FundersHakai Institute
KeywordsInvertebrateMarine invertebratesTrophic levelMarine ecosystemNoise (video)Environmental scienceEcologyRange (aeronautics)UnderwaterBiologyOceanographyEcosystemComputer scienceGeologyEngineering

Abstract

fetched live from OpenAlex

Human-generated noise over the last 60 years has increased concerns regarding the implications for marine species. Many species have been documented to display behavioral and physiological responses to increased noise pollution in our oceans, but the majority of this research has focused on higher trophic organisms. Recently, investigations assessing the impacts of changing soundscapes on entire aquatic ecosystems have begun. To understand the impacts of underwater noise on invertebrate communities, a meta-analysis was conducted on the behavioral and physiological impacts of noise on invertebrates. A systematic review of the literature revealed 1,105 potential studies, which 25 were extracted for data analysis. The studies resulted in 473 data points evaluating the impacts of a plethora of acoustic stimuli on a wide range of marine invertebrate taxa. Here, two acoustic stimuli (ship noise and seismic surveys) were further broken down into behavioral and physiological parameters. Shipping noise had a negative effect size on the behavior and physiology of marine invertebrates. However, seismic surveys resulted in a positive effect size, which was not predicted. While further analysis is required to understand the impacts of these stimuli fully, this meta-analysis reveals the implications that elevated underwater noise levels may have on marine invertebrate communities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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.

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

Citations22
Published2019
Admission routes2
Has abstractyes

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