MétaCan
Menu
Back to cohort
Record W4318481946 · doi:10.1111/fme.12615

Effectiveness of barotrauma mitigation methods in ice‐angled bluegill and black crappie

2023· article· en· W4318481946 on OpenAlexafffund
Michael J. Louison, Luc LaRochelle, Steven J. Cooke

Bibliographic record

VenueFisheries Management and Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersCanada Foundation for Innovation
KeywordsLepomis macrochirusFish <Actinopterygii>FisheryFishingEnvironmental scienceLepomisBiology

Abstract

fetched live from OpenAlex

Abstract Barotrauma can lead to physical injury and physiological disturbance (elevated stress hormones, and depleted energy stores during post‐release struggling) in angled fish. Effectiveness of methods for reducing effects of barotrauma on fish has not been tested on fish subjected to ice‐angling. We examined post‐release behavior and re‐descension of bluegill Lepomis macrochirus and black crappie Pomoxis nigromaculatus . Barotrauma was mitigated for fish either during capture by slow retrieval or following capture by venting or re‐descension with weights, before observation in a behavioral arena or using small acceleration and depth biologgers. Black crappie spent less time in the center of the behavioral arena and were less likely to successfully re‐descend than bluegill. Depth increased over time during the post‐release monitoring period, with control fish less likely to descend to depth as fish for which barotrauma was mitigated. Our results demonstrate species‐specific effects of ice‐angling to inform anglers on the effectiveness of barotrauma mitigation strategies to improve welfare of fish after release.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.036
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFisheries Management and EcologySame topicFish Ecology and Management StudiesFrench-language works237,207