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Record W4412862257 · doi:10.1016/j.fishres.2025.107480

Release mortality in Pacific salmon fisheries along the homing migration and recommended best practices to maximize welfare and survival

2025· article· en· W4412862257 on OpenAlexaff
Tanya S. Prystay, Emma L. Lunzmann-Cooke, Stephen D. Johnston, Kurt R. Zinn, Brian Hendriks, Steven J. Cooke, David A. Patterson, S. G. Hinch

Bibliographic record

VenueFisheries Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSimon Fraser UniversityFisheries and Oceans CanadaCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsHoming (biology)FisheryWelfarePacific oceanOceanographyGeographyEnvironmental scienceEconomicsBiologyEcologyGeology

Abstract

fetched live from OpenAlex

Release or discard of captured fish commonly occurs in commercial and recreational fisheries and can result in immediate fish mortality during capture or delayed mortality upon release. This review synthesizes data from Pacific salmon ( Oncorhynchus spp.) fisheries examining intrinsic and extrinsic factors affecting Pacific salmon individual release mortality (RM) across species and fishing sectors as adult fish mature and transit from marine, to estuarine, to fresh water. RM risk was high (26–45 % observed mortality) in all fisheries and environments when captured fish were bleeding, had high levels of scale loss, had fin or eye damage, and were exposed to low oxygen from net crowding and exhaustion. Highest RM risk (>45 % observed mortality) was associated with gill net and purse seine fisheries. Air exposure and handling duration contributed to high RM when water temperatures in any environment exceeded 18°C. Estuarine and lower river environments have elevated RM risk due to osmotic, maturation, and temperature changes. Short to medium term (≤24 h) observations were poor predictors of longer-term RM, and observations of at least 5–10 days were needed to assess more complete RM rates. RM mechanisms were environment, fishery sector, and life-stage specific. Our best practice recommendations for modifying current fishing practices are gear- and location-specific and aim to minimise stress, injury, and bycatch, which could result in improvements to fish welfare, reductions in RM, and associated conservation benefits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.080
GPT teacher head0.349
Teacher spread0.268 · 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 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

Citations4
Published2025
Admission routes1
Has abstractyes

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