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Record W2272316893 · doi:10.22004/ag.econ.198436

Efecto sobre el comercio y bienestar de distintas estrategias tecnológicas para el arroz uruguayo.

2012· article· es· W2272316893 on OpenAlexfundno aff
Federico García Suárez, Bruno Lanfranco, Guy Hareau

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2012
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersRisk Management AgencyOntario Ministry of Food and AgriculturePurdue UniversityMonash UniversityUniversity of CambridgeInstituto Nacional de Investigación y Tecnología Agraria y AlimentariaEconomic Research ServiceForeign Agricultural ServiceU.S. Department of Agriculture
KeywordsHumanitiesGeographyPolitical scienceAgricultural scienceArtBiology

Abstract

fetched live from OpenAlex

El principal objetivo de este trabajo fue la evaluación del impacto económico potencial generado por diferentes alternativas tecnológicas de producción de arroz en el Uruguay: adopción de buenas prácticas de manejo agronómico en el cultivo (BPM) e incorporación de variedades transgénicas (GM), en el marco de un mercado internacional segmentado por cambios en las preferencias de los consumidores de algunos países importadores del cereal. Se analizaron los cambios en el comercio del arroz a través de un modelo de equilibrio general computable. Se cuantificaron los flujos de comercio internacional para arroz cáscara y arroz procesado, entre las doce regiones consideradas: Uruguay, Brasil, NAFTA, Resto de América, Europa, África, Tailandia, China, Japón, Medio Oriente, Resto de Asia y Resto del Mundo. La simulación se llevó a cabo sobre la base de escenarios que consideraron distintos tipos de BPM y de cambios en las preferencias por productos GM, por parte de los consumidores en Brasil y la Unión Europa. Los resultados sugieren que la adopción de BPM en el cultivo de arroz, realizada hasta el momento en el país, produjo mejoras en el bienestar de la economía, en tanto la incorporación de GM supondría un riesgo de pérdida de las mejoras obtenidas.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.253
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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