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Record W1968732738 · doi:10.1121/1.3177276

Hydroacoustic measurements of the behavioral response of arctic riverine fishes to seismic airguns

2009· article· en· W1968732738 on OpenAlexafffund
John K. Jorgenson, Eric C. Gyselman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsFisheries and Oceans Canada
FundersFisheries Joint Management Committee
KeywordsHerdingFish <Actinopterygii>Noise (video)Sound (geography)ArcticEnvironmental scienceGeologyFront (military)OceanographyAcousticsFisherySeismologyComputer scienceGeographyBiologyPhysics

Abstract

fetched live from OpenAlex

Seismic surveys for hydrocarbon exploration in the Mackenzie River involve the use of airguns. Airguns produce a repetitive, intense, low-frequency sound that has the potential to cause physiological damage and behavioral changes in fishes. Some of these impacts have been documented in marine environments but few studies have been conducted in freshwater systems where the confining nature of the environment produces a different acoustic regime and could constrain possible fish response. In the current study, hydroacoustic surveys are conducted in the presence of airgun firing in the Mackenzie River to determine if fish behavior can mitigate or enhance the potential impact of this sound. It is shown that fish behavioral characteristics measured in this study are generally not changed by the presence of airgun noise. The most likely mechanism to facilitate a severe physiological effect in fishes from a mobile airgun firing is a herding response in front of the airgun, resulting in prolonged exposure to the noise. Analysis of tracked fish directional movement does not indicate that herding behavior occurs. Consequently, no evidence is found to indicate that fishes in this study would sustain severe physiological damage from this airgun seismic survey.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.280
Teacher spread0.243 · 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 designSimulation or modeling
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

Citations16
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207