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Seasonal patterns in bycatch composition and mortality associated with a freshwater hoop net fishery

2011· article· en· W1793257462 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnimal Conservation · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsMinistry of Natural Resources and ForestryFisheries and Oceans CanadaCarleton UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Natural ResourcesWorld Wildlife Fund
KeywordsBycatchFisheryFishingPopulationTurtle (robot)Fish mortalityBiologyCatch per unit effortGeographyEcologyEnvironmental scienceFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Abstract Although bycatch is well known and well studied in marine fisheries, comparatively little is known about bycatch in freshwater fisheries. Even basic information on bycatch composition and mortality in freshwater is unavailable, given that few inland jurisdictions require reporting of bycatch. A small‐scale inland hoop net fishery that targets pan fish (e.g. sunfish, L epomis spp.) and operates primarily in the spring and fall was simulated in two lakes in south‐eastern O ntario to characterize both bycatch composition and mortality. We fished one lake in both spring and fall to compare catch rates, while in the other lake we set nets for 2 or 6 days during the spring to assess fish mortality associated with different net tending frequencies. In both lakes, bycatch consisted of gamefish, turtles (including several species at risk), and mammals. For fish, there was no difference in spring and fall catches. Turtles, however, were captured more often in spring. Fish mortality of both target and non‐target species increased from 0.3–0.9% to 3.0–3.7% (4–10 times) when set net duration increased from 2 to 6 days. Despite the provision of an air breathing space in our nets, we documented severe turtle mortality (33% in one lake) and all mammals died, suggesting that provision of air spaces is not always effective. Although all bycatch mortality is a concern, turtles are prone to population declines even with low levels of non‐natural mortality. As such, regulators may consider limiting commercial fishing to the fall in this region to reduce turtle captures. Seasonal restrictions on fishing or use of frequent net tending (e.g. &lt; 2 days) will not prevent all turtle bycatch and therefore gear modifications should be investigated to further reduce turtle captures and mortality associated with hoop nets.

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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 score0.014
Threshold uncertainty score0.985

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 head0.031
GPT teacher head0.218
Teacher spread0.187 · 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