An analysis of production efficiency of cow-calf operations in Alberta
Bibliographic record
Abstract
The purpose of this study is to examine the production efficiency (i.e., technical, allocative and economic) of cow-calf farms in Alberta. Production efficiencies are measured using an econometrically estimated stochastic Cobb-Douglas production frontier and analytically derived stochastic cost frontier. The study uses repeated cross-section data of samples of 333 Alberta cow-calf farms from 1995 to 2002. The results reveal that mean technical, allocative and economic efficiencies for sample cow-calf farms are approximately 83, 78, and 67 percents, respectively. ' Ceteris paribus', such degrees of production efficiency suggest that Alberta cow-calf producers could increase output and/or save cost by reallocating resources with the existing technology. Improvement in allocative efficiency appears to be relatively more important than technical efficiency as a source of gains in production efficiency for the sample cow-calf farms. The results suggest that herd size and biological efficiency have positive effects on production efficiency; government supports and production efficiency are negatively related; and there is variation in production efficiency across farms in different locations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".