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Record W2136057233 · doi:10.1577/m05-050.1

Swimming Performance of Brook Trout after Simulated Catch-and-Release Angling: Looking for Air Exposure Thresholds

2005· article· en· W2136057233 on OpenAlexaff
Jason F. Schreer, Dayna M. Resch, Malachy L. Gately, Steven J. Cooke

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersCampus Research Board
KeywordsSalvelinusTroutCatch and releaseFishingEnvironmental scienceFontinalisFisheryFish <Actinopterygii>PredationExposure durationBrown troutAnimal scienceToxicologyBiologyEcologyRecreational fishing

Abstract

fetched live from OpenAlex

Abstract Air exposure has been hypothesized as one of the primary stressors present during catch-and-release angling. However, there are few studies that systematically vary air exposure duration and evaluate the consequences on individual fish. Here, we evaluated the short-term, sublethal effects of exercise (to simulate angling) and air exposure on the swimming performance of hatchery brook trout Salvelinus fontinalis at 10°C. The duration of the angling event (i.e., chasing the fish by hand) was held constant at 30 s, while air exposure duration was systematically varied between 0, 30, 60, and 120 s. The results showed that air exposures of 60 s or less did not affect swimming performance. However, air exposure of 120 s resulted in a dramatic (∼75%) reduction in swimming performance. In fact, nearly half of the fish held out of the water for 120 s were unwilling or unable to swim at all. No mortality was observed after any of the treatments (fish were monitored for 3 months). This work suggests that fish possess air exposure thresholds that, once exceeded, result in performance impairments. Fish released after extended air exposure may become easy prey for predators or could be displaced downstream by flows in fluvial environments. We conclude that air exposure should be restricted to less than 60 s and ideally should be avoided entirely.

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)0.000
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 score0.064
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations68
Published2005
Admission routes1
Has abstractyes

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