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Record W2384777720

A review of overseas recreational fishery management

2013· review· en· W2384777720 on OpenAlexaboutno aff
Yuchen Zhang

Bibliographic record

VenueChinese Fisheries Economics · 2013
Typereview
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationRecreational fishingFishingFisheryFisheries managementBusinessMainstreamEnvironmental resource managementNatural resource economicsEnvironmental planningGeographyEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

From 1980s to the present,recreational fishery has been developed rapidly in the world,especially in countries with developed fishery,such as the United States,Japan,Australia, Canada and European countries.It has played an increasingly important role in the development of the fishing industry.Literatures at home and abroad on fishery management are collected and the current management status of overseas recreational fishery is introduced.Overall,the guiding ideology of overseas recreational management is sustainable development.Under the guidance of this thought,foreign countries tested and explored several models of fishery management.At last,the fishery quota management system became a mainstream model,being applied to all aspects of the management of recreational fishery.Also the experience in the development of overseas recreational fishery management is summarized,so as to provide a reference for the domestic recreational fishery management.Suggestions are recommended that the development of recreational fishery management in our country should be improved from the ideology,management system and other relevant aspects,in order to guarantee the healthy development of recreational fishery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.333
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2013
Admission routes1
Has abstractyes

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