Assessing Producer Stated Preferences for Identity Preservation in the Canadian Grain Handling and Transportation System
Bibliographic record
Abstract
Agricultural biotechnology will create a new set of challenges for the bulk grain handling and transportation system (GHTS) in Canada. The implementation of a credible grain identity preservation system to segregate genetically modified (GM) from non‐GM grain remains an important and unresolved issue for the industry. Furthermore, the attitude of producers toward the design of an identity preserved grain supply chain is not well understood. Using a 2003 survey of Saskatchewan grain farmers developed by the authors, we employ conjoint analysis to evaluate producer attitudes and trade‐offs among four hypothetical grain handling systems. The results indicate that farmers in the region will require significant economic incentives to adopt on‐farm segregation methods when compared to methods that segregate grain at the elevator level. Les biotechnologies agricoles vont engendrer de nouvelles préoccupations pour le système de manutention et de transport du grain (SMTG) en vrac au Canada. La mise en uvre d'un système de ségrégation des céréales génétiquement modifiées (GM) et non génétiquement modifiées (NGM) demeure un problème important non résolu pour l'industrie. De plus, l'attitude des producteurs envers la mise en place d'une chaîne d'approvisionnement de céréales à identité préservée n'est pas bien comprise. À l'aide d'un sondage que nous avons mis au point et effectué auprès des producteurs de céréales de la Saskatchewan, nous utilisons l'analyse conjointe pour évaluer l'attitude et les options des producteurs par rapport à quatre systèmes hypothétiques de manutention des céréales. Les résultats ont montré que les producteurs de la province exigeront d'importants stimulants économiques pour adopter des méthodes de ségrégation à la ferme comparativement aux méthodes de ségrégation en vigueur aux silos à céréales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".