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Record W4411462459 · doi:10.1080/23308249.2025.2518167

On the behavior of Fish Released Following Fisheries Capture: Methods, Endpoints, and Consequences

2025· article· en· W4411462459 on OpenAlexaff
Luc LaRochelle, Lucas P. Griffin, Jacob W. Brownscombe, Chris K. Elvidge, Caleb T. Hasler, Jessica A. Robichaud, Jamie C. Madden, Sean J. Landsman, Vincent Raoult, Andy J. Danylchuk, Steven J. Cooke

Bibliographic record

VenueReviews in Fisheries Science & Aquaculture · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of WinnipegFisheries and Oceans CanadaCarleton University
Fundersnot available
KeywordsFisheryFish <Actinopterygii>Environmental scienceBiology

Abstract

fetched live from OpenAlex

Quantifying behavior of fish following fisheries interactions can improve the understanding of sublethal and lethal consequence with implications for ecology, fish welfare, and sustainability of fisheries. Behavior involves the integration of the peripheral or central nervous system in response to stimulus or stimuli (varied challenges experienced by fish during capture in this case) that produces coordinated motor actions from the animal. Methods used to assess behavior of fish captured in recreational or commercial fisheries include behavioral arenas and mazes, human constructed systems such as mesocosms, underwater and above water video recordings or observations (remotely operated vehicles, swimmers), telemetry (radio and acoustic transmitters), and biologgers (often with accelerometer sensors) are synthesized. Endpoints assessed were swimming activity, distance, depth and temperature selection, migration success, refuge seeking or conspecific schools, predator avoidance, and body orientation. Suggestions on new methods, or ways to improve current techniques on assessing the behavior of fish are provided.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
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 head0.031
GPT teacher head0.303
Teacher spread0.273 · 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 designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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