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Record W2612244213 · doi:10.4038/sljas.v10i0.7456

Fisheries, fish culture, tilapias and politics

2005· article· en· W2612244213 on OpenAlexaff
C. H. Fernando, José Jarbas Studart Gurgel

Bibliographic record

VenueSri Lanka Journal of Aquatic Sciences · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFishingFisheryChinaAgricultureTilapiaFish <Actinopterygii>Fish stockAquacultureFish farmingFishing industryGeographyBiologyEcology

Abstract

fetched live from OpenAlex

In the past few years much attention has been focused on dwindling marine fish stocks and the means available to counter the effects of this growing problem. Over fishing has been targeted as the main cause. Many newspaper and magazine articles on the subject of over fishing have been added to the growing scientific journal publications and books. Conservation measures are being implemented but their impacts will very likely not reverse the trend. One option to provide more fish for human consumption is through fish culture which, since traditional techniques began to be modernized in earnest in the 1950's, has been expanding rapidly. With the exception of production in China, efforts with the major carps have not been very productive, especially in tropical countries, and these and several other fishes currently in culture have relatively limited possibilities of substituting the shortfall of marine fish in the future. The tilapias, on the other hand, have shown much greater potential recently, and are increasingly marketed worldwide. In two or three decades, farming tilapias may prove to be the means of supplying a large quantity of fish to fill the increasing gap between demand and supply. Furthermore, tilapias can contribute additional resources through integration with traditional reservoir fisheries and rice-fish culture. This paper explores these growing changes in tilapia culture and inland fisheries especially in the tropics. We do not claim any deep knowledge nor are we predicting the economical and sociological impacts of the winds of change affecting fisheries worldwide.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.303
Teacher spread0.266 · 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
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

Citations0
Published2005
Admission routes1
Has abstractyes

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