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Record W4220656555 · doi:10.1073/pnas.2114932119

Behavioral responses to predatory sounds predict sensitivity of cetaceans to anthropogenic noise within a soundscape of fear

2022· article· en· W4220656555 on OpenAlexfundno aff
Patrick J. O. Miller, Saana Isojunno, Eilidh Siegal, Frans‐Peter A. Lam, Petter H. Kvadsheim, Charlotte Curé

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersOffice of Naval ResearchDefence Science and Technology GroupDefence Science and Technology LaboratoryU.S. NavyUniversity of VictoriaAarhus Universitet
KeywordsForagingPredationSoundscapeEcologyHabitatNoise pollutionBiologyNoise (video)Environmental scienceSound (geography)OceanographyAcousticsComputer science

Abstract

fetched live from OpenAlex

SignificanceAcoustic signals travel efficiently in the marine environment, allowing soniferous predators and prey to eavesdrop on each other. Our results with four cetacean species indicate that they use acoustic information to assess predation risk and have evolved mechanisms to reduce predation risk by ceasing foraging. Species that more readily gave up foraging in response to predatory sounds of killer whales also decreased foraging more during 1- to 4-kHz sonar exposures, indicating that species exhibiting costly antipredator responses also have stronger behavioral reactions to anthropogenic noise. This advance in our understanding of the drivers of disturbance helps us to predict what species and habitats are likely to be most severely impacted by underwater noise pollution in oceans undergoing increasing anthropogenic activities.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.045
GPT teacher head0.311
Teacher spread0.267 · 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 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

Citations56
Published2022
Admission routes1
Has abstractyes

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