Bibliographic record
Abstract
An analytical model was developed to explore prospective costs and risks of alternative testing strategies for a marketing system in Canada which markets both genetically modified (GM) and Non‐GM wheats. The problem is solved using stochastic optimization, base case results are defined, and sensitivities conducted to evaluate impacts of selected variables. Added costs include: testing, rejection, and a risk premium which is required for handlers to be indifferent between the current and the proposed dual system. Protocols would require testing at the point of loading at the primary elevator, and export elevator, and supplementing this information with some form of grower variety declaration. There are several sources of inherent risks in such a system. For buyers, the cumulative impact of these is the risk of receiving GM content in a Non‐GM shipment. For sellers, it is the risk of having a Non‐GM shipment rejected. For sellers, the risk of rejection was typically less than 2%, and for buyers, the risk was typically less than 0.02%. Nous avons élaboré un modèle analytique pour explorer les coûts et les risques potentiels de la mise en place de nouvelles stratégies pour analyser le grain si le Canada décidait de commercialiser du blé génétiquement modifié (GM) et du blé non génétiquement modifié (NGM). Le problème est résolu à l'aide d'une optimisation stochastique; des scénarios de référence sont définis et des tests de sensitivité sont effectués pour évaluer l'impact de variables sélectionnées. Les coûts supplémentaires comprennent les coûts d'analyses, les coûts liés au rejet ainsi qu'une prime de risque exigée pour que les manutentionnaires demeurent indifférents entre le système actuel et le système double proposé. Les protocoles obligeraient la tenue d'analyses au point de chargement du silo primaire ainsi qu'au silo terminal, auxquelles s'ajouterait une certaine forme de déclaration du céréaliculteur sur la variété. Ce genre de système comporte plusieurs sources de risques inhérents. Pour les acheteurs, l'impact cumulatif est le risque de recevoir un chargement de blé NGM contenant du blé GM. Pour les vendeurs, c'est le risque qu'un chargement de blé NGM soit rejeté. Pour les vendeurs, le risque de rejet était généralement inférieur à 2%, et pour les acheteurs, le risque était généralement inférieur à 0.02%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".