Integration to Fragmentation: Post‐BSE Canadian Cattle Markets, Processing Capacity, and Cattle Prices
Bibliographic record
Abstract
This paper examines the potential impacts of expansions to Canadian cattle slaughter capacity with varying assumptions about the ability to export live cattle to the United States. A synthetic model is calibrated to historic data and then used to gauge the impacts of changing slaughter capacity, commercial grade beef import competition, and export potential for lower quality cuts on the Canadian cattle and beef sector. Expanded slaughter capacity improves fed cattle prices, but cull prices remain below pre‐BSE levels. Reduced ability to export lower quality beef and increased import competition from commercial grade beef also further depress cattle prices. Le présent article examine les répercussions potentielles d'une augmentation de la capacité d'abattage au Canada à l'aide de diverses hypothèses sur la capacité d'exporter des bovins vivants aux États‐Unis. Un modèle synthétique est calibré selon les données historiques et utilisé pour évaluer les répercussions qu'une modification de la capacité d'abattage, de la concurrence quant à l'importation de bœuf de qualité commerciale et de l'exportation éventuelle de coupes de viande de qualité inférieure aurait sur le secteur canadien du bœuf. Une capacité d'abattage accrue améliore les prix des bovins finis, mais les prix des animaux de réforme demeurent inférieurs aux niveaux de prix observés avant la découverte de l'ESB. La diminution de la capacité d'exporter du bœuf de qualité inférieure et l'augmentation de la concurrence pour l'importation de bœuf de qualité commerciale contribuent également à faire baisser davantage les prix des bovins.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".