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Record W2510616720 · doi:10.1111/fme.12191

Recommendations for the future of recreational fisheries to prepare the social‐ecological system to cope with change

2016· article· en· W2510616720 on OpenAlexaff
Robert Arlinghaus, Steven J. Cooke, Stephen G. Sutton, Andy J. Danylchuk, Warren M. Potts, Kátia Meirelles Felizola Freire, Josep Alós, E. T. da Silva, I. G. Cowx, Raymon van Anrooy

Bibliographic record

VenueFisheries Management and Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsRecreationFisheries lawFisheries managementCorporate governanceFishingBusinessFisheries scienceScale (ratio)Environmental resource managementFisheryEnvironmental planningRecreational fishingAdaptive managementAgricultureGeographyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract This paper presents conclusions and recommendations that emerged from the 7th World Recreational Fishing Conference ( WRFC ) held in Campinas, Brazil in September 2014. Based on the recognition of the immense social and economic importance of recreational fisheries coupled with weaknesses in robust information about these fisheries in many areas of the world, particularly in many economies in transition, it is recommended to increase effort to build effective governance arrangements and improve monitoring and assessment frameworks in data‐poor situations. Moreover, there is a need to increase interdisciplinary studies that will foster a systematic understanding of recreational fisheries as complex adaptive social‐ecological systems. To promote sustainable recreational fisheries on a global scale, it is recommended the detailed suggestions for governance and management outlined in the United Nations Food and Agricultural Organization Technical Guidelines for Responsible Fisheries: Recreational Fisheries are followed.

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 score0.767
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.026
GPT teacher head0.234
Teacher spread0.208 · 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

Citations110
Published2016
Admission routes1
Has abstractyes

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