MétaCan
Menu
Back to cohort
Record W3037707959 · doi:10.1080/23308249.2020.1770689

Knowledge Gaps and Management Priorities for Recreational Fisheries in the Developing World

2020· article· en· W3037707959 on OpenAlexaff
Shannon D. Bower, Øystein Aas, Robert Arlinghaus, T. Douglas Beard, I. G. Cowx, Andy J. Danylchuk, Kátia Meirelles Felizola Freire, Warren M. Potts, Stephen G. Sutton, Steven J. Cooke

Bibliographic record

VenueReviews in Fisheries Science & Aquaculture · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecreationFishingRecreational fishingFisheries lawFisheries managementFisheryBusinessGeographyDeveloping countryEnvironmental resource managementCommercial fishingAgricultureEnvironmental planningNatural resource economicsEconomic growthEcologyEconomics

Abstract

fetched live from OpenAlex

Millions of individuals worldwide rely on recreational fishing activities for leisure, food, and employment. Recreational fishing is the dominant freshwater fisheries sector in much of the highly developed world and plays a growing role in the marine realm, but in developing countries recreational fisheries occur within a different set of contextual conditions. Little is currently known about attributes of the recreational fishing sector in many developing countries. A survey of fisheries experts designed to identify knowledge gaps surrounding recreational fishery development was conducted to gather information on fishery attributes in developing countries. These surveys were supplemented with a review of relevant literature. Results show that recreational fishing is socially important and is expected to grow in most countries surveyed. Recreational fisheries were described as mainly consumption oriented in these regions. Most often, nonresident tourists used marine waters and resident recreational fishers used fresh waters. There was strong agreement among respondents on the need to address data deficiencies. The knowledge gaps and management needs identified can support international bodies and recreational fishing organizations (such as the regional fisheries bodies of the Food and Agricultural Organization of the United Nations, and local and international fishing associations) to support sustainable development and management of the global recreational fisheries sector.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.560
Threshold uncertainty score0.451

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 communication0.0000.001
Open science0.0010.000
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 head0.065
GPT teacher head0.290
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations48
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueReviews in Fisheries Science & AquacultureSame topicFish Ecology and Management StudiesFrench-language works237,207