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Record W2133413704 · doi:10.1080/02755947.2015.1083495

Evaluating Short Openings as a Management Tool to Maximize Catch-Related Utility in Catch-and-Release Fisheries

2015· article· en· W2133413704 on OpenAlexaff
Edward V. Camp, Brett T. van Poorten, Carl J. Walters

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaMinistry of Environment
FundersFlorida Sea Grant, University of FloridaDivision of Graduate EducationUniversity of FloridaFlorida Fish and Wildlife Conservation Commission
KeywordsFishingFisheryRecreational fishingCatch and releaseFisheries managementRecreationFish <Actinopterygii>Key (lock)BusinessEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Catch and release (CR) is an increasingly common strategy for recreational fisheries in which sustaining high catch rates is important. The success of this strategy is reduced if the released fish are temporarily invulnerable to capture due to behavioral changes, as recent research suggests. Here, we explore how temporary fishing closures with short openings might be used in CR fisheries to increase the catch-related utility associated with angler satisfaction from catches. We simulated generic fisheries in single-lake and multiple-lake systems and found that regular, temporary closures could increase catch-related utility—but predominately under the key assumption that angler satisfaction increases disproportionately with increasing catch rates. In the multiple-lake case, a strategy of rotating temporary closures could provide greater catch-related utility than continuously open fisheries, but this would depend upon anglers' willingness to redistribute effort from closed waters to open waters. A key implication of these results is that even in CR fisheries, effort limitation may be necessary to provide quality angling opportunities. Our results also emphasize the importance of understanding how vulnerable pool dynamics can differ across fisheries and potentially interact with other processes and mechanisms that drive the observed changes in catchability and catch rates.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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