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Record W1467993868 · doi:10.1016/j.aaspro.2015.08.011

Factors Impact on Farmers’ Adaptation to Drought in Maize Production in Highland Area of Central Vietnam

2015· article· en· W1467993868 on OpenAlexfundno aff
Tran Cao Uy, Budsara Limnirankul, Yaovarate Chaovanapoonphol

Bibliographic record

VenueAgriculture and Agricultural Science Procedia · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
FundersGraduate School, Chiang Mai UniversityInternational Development Research Centre
KeywordsMultinomial logistic regressionProduction (economics)Adaptation (eye)GeographyScale (ratio)Agricultural scienceSocioeconomicsAgricultural economicsEconomicsBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper assesses factors impact on adaptation to drought in maize production of farmers in Dakrong district – a highland district of Central Vietnam. The study was conducted with 180 farmers, being selected randomly from three communes of the district. Factor analysis and Multinomial Logit regression indicated six main groups of factors impacting on four separate adaptation options (called ADP) in maize production in the area. In which, the increasing in household's capitals, experience, maize production scale, and gender and non-farm income significantly increased the probability of applying at least one adaptation measure in maize production of the farmers.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.235
Teacher spread0.211 · 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 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

Citations14
Published2015
Admission routes1
Has abstractyes

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