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Record W3123811190

Risk Attitudes, Social Interactions and the Adoption of Genotyping in Dairy Production

2014· preprint· en· W3123811190 on OpenAlexfundno aff
Xi Yu, Getu Hailu, Jessica Cao

Bibliographic record

VenueThe Atrium (University of Guelph) · 2014
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
FundersUniversity of AlbertaOntario Ministry of Agriculture, Food and Rural AffairsDairy Farmers of OntarioGenome Canada
KeywordsGenotypingProduction (economics)BusinessMastitisMilk productionWillingness to payService (business)Survey data collectionMarketingEconomicsGenotypeBiologyGeneticsMicroeconomicsAnimal scienceStatistics
DOInot available

Abstract

fetched live from OpenAlex

Risk attitudes and social interactions have been of increasing interest to economists in the understanding of new technology adoption. This study examines the impacts of risk attitude and social interaction on the willingness to pay (WTP) for DNA genotyping service for mastitis susceptibility. Since the technology is not available to farmers, contingent valuation with double bounded dichotomous choice questions have been applied to reveal producers’ potential willingness to pay (WTP) for DNA genotyping and assume that the WTP may serve as an indicator of planned adoption decision. The mean WTP for DNA genotyping is estimated around $50. Both risk tolerance and social interactions are found to have positive impact on potential WTP for the technology. Specifically, producers’ risk attitudes towards professional activity and health are shown to have the most significant impact on the WTP. In addition, the results also highlight the interaction effect between risk attitudes and social interactions.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.236
Teacher spread0.207 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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