Labor Use and Profitability Associated with Pasture Systems in Grass-Fed Beef Production
Bibliographic record
Abstract
Three pasture systems for grass-fed beef that are representative of those used in the U.S. Gulf Coast region are compared by labor use and profitability. In addition to means comparisons, stochastic efficiency with respect to a function analysis allows us to incorporate the role of risk preference in determining the most preferred production system. Five years of experimental data from the Iberia Research Station in Louisiana are used to develop revenue, expense, and labor use estimates for the three systems. Results suggest that, with or without including charges for labor, the most profitable system is the least complex bermudagrass-ryegrass system. If labor is included, a medium-complexity forage system becomes preferred for more risk averse farmers. The most complex forage system might become competitive if a carbon market were developed and/or farmers were able to realize higher grass-fed beef prices on the basis of product quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".