The Economics of Grain Cleaning on the Prairies
Bibliographic record
Abstract
In the Canadian grain marketing system, grains have typically been cleaned to very tight levels at port elevators. In recent years, changes in rail rates, livestock production and grain handling technology are pressuring the system for change. A mathematical programming model of vertical marketing functions in western Canada is developed to determine optimal locations for cleaning. Cleaning margins are positive in all regions of the prairies. Grain cleaning is found generally to be more efficient on the prairies than at the ports. However, grain cleaning will continue at ports to some extent because of inadequate capacity at country positions and sunk costs at port facilities and because primary elevators will likely clean only the dominant grains. Dans le système canadien de commercialisation des céreales, les grains passent généralement par un nettoyage très rigoureux aux silos portuaires. Ces dernières années, les changements affectant les tarifs ferroviaires, les productions animales et la technologie de manutention des céréales rendent cependant nécessaire un changement du système. Les auteurs utilisent un modéle mathématique de programmation des fonctions verticales de mise en marché pour déterminer les emplacements idéaux pour le nettoyage du grain. Les marges commerciales des nettoyeurs étaient positives dans toutes les régions des Prairies et le nettoyage était généralement plus efficient dans cette partie di pays qu'aux situations portuaires. Il continuera cependant à sefaire à ces derniers endroits dans une certaine mesure, en raison du manque de capacités suffisantes aux emplacements de campagne, des coûts irrécupérabies aux installations portuaires et du fait que les élévateurs primaires ne nettoieront vraisemblablement que les céréales commercialement le plus importantes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".