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Record W1947926440 · doi:10.5539/jas.v7n10p101

“Live Tilapia”: Diversifying Livelihoods for Rural Communities in México

2015· article· en· W1947926440 on OpenAlexvenueno aff
Verónica Lango-Reynoso, Juan L. Reta-Mendiola, Alberto Asiaín-Hoyos, Katia A. Figueroa-Rodríguez, Fabiola Lango‐Reynoso

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodBusinessAquacultureConsumption (sociology)PopulationTilapiaSocioeconomic statusNatural resource economicsEconomic growthAgricultural economicsFish <Actinopterygii>FisheryGeographyEconomicsAgriculture

Abstract

fetched live from OpenAlex

This study documents the socioeconomic impact of an innovation based on the marketing of live aquaculture products in rural communities as the Live Tilapia Points of Sales (LTPOS). A case study research conducted where the participatory strategy of “Simultaneous Production Growth Groups” (SPGG) was applied for technology innovation, which includes technology modules, organizational strategy and operational management. The results were evaluated socially and economically. In 14 months, 3531.50 kg of live tilapia were distributed in four local places for sale and self-consumption. The system obtained a BCR of 1.23 and offered as well to the rural population an alternative of fish consumption in better freshness conditions than the regular fish supply. It brings up an extra income to the main market chain participants and development of aquaculture capabilities. Therefore, the LTPOS is a viable option for diversifying livelihoods and improving regional rural population income.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.259
Teacher spread0.213 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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