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Record W4402771546 · doi:10.1139/facets-2024-0031

A One Health perspective on recreational fisheries

2024· article· en· W4402771546 on OpenAlexafffundvenue
Steven J. Cooke, Andy J. Danylchuk, Joel Zhang, Vivian M. Nguyen, Len M. Hunt, Robert Arlinghaus, Kathryn J. Fiorella, Hing Man Chan, Tony L. Goldberg

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of OttawaMinistry of Natural Resources and ForestryCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaGreat Lakes Fishery Commission
KeywordsRecreational fishingPerspective (graphical)FisheryRecreationFisheries scienceBusinessEnvironmental planningFish <Actinopterygii>Environmental scienceEnvironmental resource managementFisheries managementFishingComputer scienceEcologyBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Recreational fisheries involve an intimate connection between people, individual fish, and the environment. Recreational fishers and their health crucially depend on healthy fish and ecosystems. Similarly, fish and ecosystems can be impacted by the activities of people including recreational fishers. Thus, amplified by the global interest in recreational fishing, we posit that recreational fishing is particularly suited as an empirical system to explore a One Health perspective, with a goal of creating pathways to better manage such socio-ecological systems for the benefit of people, fish, and the environment. Although zoonoses are uncommon in fishes, fish can carry pathogens, biotoxins, or contaminants that are harmful to people. When captured and released, fish can experience stress and injuries that may promote pathogen development. Similarly, when humans contribute to environmental degradation, not only are fish impacted but so are the humans that depend on them for nutrition, livelihoods, culture, and well-being. Failure to embrace the One Health perspective for recreational fisheries has the potential to negatively impact the health of fish, fisheries, people, society, and the aquatic environment—especially important since these complex social–ecological systems are undergoing rapid change.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.999

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)0.0020.000

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 head0.051
GPT teacher head0.370
Teacher spread0.318 · 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.

Study designNot applicable
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

Citations5
Published2024
Admission routes3
Has abstractyes

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