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Record W3127274063 · doi:10.1002/nafm.10489

Physiological Consequences of Different Fishing Tournament Culling Methods on Largemouth Bass

2021· article· en· W3127274063 on OpenAlexafffund
Daniel R. Chong, Alice E.I. Abrams, Aaron J. Zolderdo, Michael Lawrence, Connor H. Reid, Steven J. Cooke

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of ManitobaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCullingMicropterusBiologyBass (fish)FisheryFish <Actinopterygii>Animal scienceHerd

Abstract

fetched live from OpenAlex

Abstract In live-release angling tournaments, fish are captured and typically held within onboard live-well systems, where they are subsequently “culled” (i.e., released) as larger fish are captured. Anglers often mark individual fish to easily identify them based on weight and to reduce handling time. However, there is limited information about the physiological consequences of using different culling apparatus on fish. This study examined the physiological consequences associated with using four different types of culling apparatus (i.e., metal stringer through the jaw, pincher on the jaw, lasso around the caudal peduncle, and zippered mesh bag) on Largemouth Bass Micropterus salmoides relative to controls during a 2-h live-well retention period. Blood samples were taken afterwards and were analyzed for blood glucose, blood lactate, plasma cortisol, and osmolality. Compared to the baseline control (i.e., fish that were captured, subjected to blood sampling, and immediately released), blood parameters (except osmolality) were significantly elevated in all treatments. The pincher and lasso treatments tended to yield higher physiological disturbances than the other treatments, including fish that were held in the live well without any culling apparatus. Moreover, the lasso culling apparatus appeared to cause noticeable injury relative to the other culling devices. Our research provides valuable information to help guide the selection of culling gear that maintains the welfare status of retained fish during tournaments.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.022
GPT teacher head0.268
Teacher spread0.245 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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