Improving the Competitiveness of Beef Production in the Northern Contiguous United States and Canada
Bibliographic record
Abstract
Problems for North American Beef ProducersThe beef production industry in North America is unlikely to benefit from any market price increases for the foreseeable future.On the contrary, real (inflation-adjusted) prices are likely to decline further in the face of greater competition from other meats and from low-cost countries such as Brazil and New Zealand as international trading becomes less restricted.This behooves North American beef producers to reduce production costs in order to remain competitive.Two possibilities for cost reductions present themselves.One is through technological progress, such as genetic engineering, embryo transplants, breeding synchronization, etc.The other is through finding more productive and efficient management practices, such as increasing calving rates, reducing the costs of stored feeds, labor and other inputs, or obtaining added value by marketing progeny at higher liveweights.This study focused on the second of these two possibilities, and in particular on examining combinations of management practices that offer alternatives to traditional production procedures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".