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Engaging the Recreational Angling Community to Implement and Manage Aquatic Protected Areas

2010· article· en· W2141660216 on OpenAlexaff
Andy J. Danylchuk, Steven J. Cooke

Bibliographic record

VenueConservation Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecreationFishingRecreational fishingSustainabilityBusinessFisheryEnvironmental resource managementEnvironmental planningGeographyEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Recreational angling is a popular leisure activity, the quality of which is greatly dependent on fish abundance and well-functioning aquatic ecosystems. Aquatic protected areas (APAs) are used to help maintain and even restore aquatic systems and their associated biota, including fish species that are popular with recreational anglers. Paradoxically, the use of APAs has been a source of much contention and conflict between members of the recreational angling community and those interested in or mandated to protect aquatic resources on the basis of the interests of multiple stakeholder groups. The angling community is concerned about the loss of fishing opportunities and effectiveness of APAs. Although it is still unclear whether establishment of APAs alone can effectively protect aquatic resources, actively including the recreational angling community in the design, implementation, and management of APAs will help ensure the values of this rather substantial user group are incorporated into aquatic conservation strategies. Conversely, the probability of increasing the sustainability of recreational angling and related economies will be greatest if recreational angler groups remain open minded to both short-term and long-term goals of fisheries conservation strategies, including the use of APAs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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