Stochastic economic evaluation of dairy farm reproductive performance
Bibliographic record
Abstract
Kalantari, A. S. and Cabrera, V. E. 2015. Stochastic economic evaluation of dairy farm reproductive performance. Can. J. Anim. Sci. 95: 59–70. The objective of this study was to assess the economic value of reproductive performance in dairy farms under uncertain and variable conditions. Consequently, the study developed methods to introduce stochasticity into transition probabilities of a Markov chain model. A robust Markov chain model with 21-d stage length and three state variables, parity, days in milk, and days in pregnancy, was developed. Uncertainty was added to all transition probabilities, milk production level, and reproductive costs. The model was run for 10 000 replications after introducing each random variable. The expected net return (US$ cow−1 yr−1±standard deviation) was $3192±75.0 for the baseline scenario that had 15% 21-d pregnancy rate (21-d PR). After verifying the model's behavior, it was run for 2000 replications to study the effect of changing 21-d PR from 10 to 30% with one-unit-percentage interval. The economic gain of changing 21-d PR from 10 to 30% resulted in a US$75 cow−1 yr−1, and this overall increase in the net return was observed mainly due to the lower reproductive and culling cost and higher calf value. The gain was even greater when milk price and milk cut-off threshold decreased.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".