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Record W3088700980 · doi:10.1002/aqc.3404

Freshwater turtle bycatch research supports science‐based fisheries management

2020· article· en· W3088700980 on OpenAlexaffabout
Sarah M. Larocque, Colin Lake, Jonathan D. Midwood, Vivian M. Nguyen, Gabriel Blouin‐Demers, Steven J. Cooke

Bibliographic record

VenueAquatic Conservation Marine and Freshwater Ecosystems · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsUniversity of OttawaCarleton UniversityFisheries and Oceans CanadaMinistry of Natural Resources and ForestryUniversity of Windsor
Fundersnot available
KeywordsBycatchTurtle (robot)FisheryResource (disambiguation)Sea turtlePopulationBusinessGeographyFishingBiologySociologyComputer science

Abstract

fetched live from OpenAlex

Abstract Although it is sometimes difficult for researchers to ensure that their work is used by resource managers to make informed decisions, an example where this knowledge–action gap has been breached is in research published in Aquatic Conservation: Marine and Freshwater Ecosystems (AQC) – among other journals – that has assisted fisheries managers in identifying strategies for reducing freshwater turtle bycatch in commercial hoop net fisheries in Ontario, Canada. Research published in AQC has provided evidence towards a simple and effective method for preventing turtle bycatch mortality in hoop nets, which could be adopted by the fishers. Other research published in AQC evaluated the effect of bycatch mortality on the probability of persistence of turtle populations with population viability analyses, and outlined the need to minimize bycatch mortality to prevent local extirpation. Nine other papers have been published on freshwater turtle bycatch in Ontario, furthering our knowledge on this issue including seasonality and temperature effects on catches, other net modifications, post‐release effects and assisted recovery, and the perspectives of fishers. The research results were presented to local resource managers with further discussions involving industry and stakeholders to minimize turtle bycatch mortality. Over several years, researchers have provided information to resource managers; however, when an incident of high turtle mortality caught the public eye, the research was readily available and changes in regulations were quick to occur. Reasonably good communication among researchers, resource managers, industry, stakeholders, and the broader public allowed the rapid implementation of regulations to mitigate freshwater turtle bycatch mortality and bridged the knowledge–action gap between researchers and resource managers. Both articles published in AQC had practical conservation impacts and were influential in providing local resource managers with feasible solutions, and the impetus to change regulations. These impacts extended to other jurisdictions and their monitoring programmes, where methods to reduce turtle bycatch mortality were also implemented.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.043
GPT teacher head0.263
Teacher spread0.220 · 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 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

Citations7
Published2020
Admission routes2
Has abstractyes

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