FED AND NON-FED CATTLE PRODUCTION RETURNS IN RELATION TO TRADE FLOW
Bibliographic record
Abstract
Percentage returns to operating capital in fed and non-fed cattle budgets for the production countries of the United States, Canada, Mexico, New Zealand, Australia, and Argentina are compiled in an effort to compare (on a percentage basis) the competitive advantage that may exist in specific production countries. The primary interest is in the production of non-fed beef, theorizing that there is need for the United States to import non-fed, lower value, beef products due to the returns available for the United States' fed beef production. A linear programming model is developed using percentage returns, and transportation costs, in the maximization of the objective function, maximizing the returns to all production countries, satisfying consumption with in production capabilities. Constraints considered are the production and consumption parameters for each production country respectively, assumptions included for the analysis of fed and non-fed beef independently. Model results indicate the most advantageous production regions and trade flows given a competitive comparison based on returns to capital. It is evident that the United States is most efficient at producing fed beef, and importing non-fed beef to satisfy demand. New Zealand is dominantly efficient at producing non-fed beef. The greater the increase in production capacity, the more efficient trade flow becomes. Government intervention is theorized to affect trade flow efficiency. Further research is needed to separate governmental impacts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".