Quality Uncertainty and Challenges to Wheat Procurement
Bibliographic record
Abstract
Issues related to quality uncertainty in wheat producing countries have escalated in importance in recent years. While Canada addresses these issues in part through variety regulations, firms in the United States resolve these through varying commercial strategies. Conventional alternatives for procurement range from spot purchases with specifications for easily measurable characteristics, to varying forms of strategies with precommitment. In grains, these are complicated by intrinsic uncertainty associated with functional qualities that are not easily measurable and that procurement costs vary spatially. Thus, shifting origins may involve higher cost due to having to bid grain away from its best market. We posed alternative procurement strategies and developed analytical models to evaluate the costs and risks of these in the case of hard red spring (HRS) wheat. Climatic conditions are a source of uncertainty in functional performance which reduces incentives for contracting and vertical integration, and poses a challenge to any form of integrated supply chain management. Les problèmes liés à l'incertitude quant à la qualité des approvisionnements des pays producteurs de blé ont augmenté au cours des dernières années. Tandis que le Canada s'attaque à ces problèmes en imposant divers règlements, des entreprises états‐uniennes les résolvent en adoptant diverses stratégies commerciales. Les moyens d'approvisionnement traditionnels varient des achats au comptant assortis de critères pour les caractéristiques facilement mesurables, à diverses stratégies comprenant un pré‐engagement. Dans le secteur des céréales, la situation est compliquée par l'incertitude intrinsèque quant aux qualités fonctionnelles qui ne sont pas facilement mesurables et le fait que les coûts d'approvisionnements varient d'un endroit à l'autre. Par conséquent, s'approvisionner dans d'autres pays pourrait entraîner des coûts plus élevés en privant le secteur de son meilleur marché. Nous avons formulé d'autres stratégies d'approvisionnement et élaboré des modèles analytiques pour évaluer les coûts et les risques de ces stratégies dans le cas du blé de force roux de printemps. Les conditions climatiques sont une source d'incertitude de la qualité fonctionnelle qui diminue les incitatifs pour la conclusion de contrat et l'intégration verticale et qui pose un obstacle pour toute forme de gestion intégrée de la chaîne d'approvisionnement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".