Effect of health status upon arrival at a single milk-fed veal facility on breakeven purchase price of calves
Bibliographic record
Abstract
Male calves are purchased from the dairy industry in North America to produce red meat. The price paid for male calves varies widely, and it is unclear which variables influence the price paid for each calf. The objective of this study was to assess how the health traits of calves at the time of arrival and demographic variables affect the breakeven purchase price of a male calf entering the veal industry. A financial model was constructed using the prevalence of health abnormalities, weight at arrival, source of the calf, number of days in the barn, base carcass price, days to mortality, feed costs, season at arrival, interest rate, housing location, carcass dressing percentage, and costs associated with housing, labor, utilities, trucking, and health to calculate the breakeven purchase price and an estimate of profit. Sensitivity analysis was conducted using health variables measured at arrival and demographic variables, including season at arrival and housing location, to identify the factors with the largest impact on the predicted average daily gain, early and late mortality risk, breakeven purchase price, and profit. At the baseline inputs, the average calculated profit was -$5.36 per calf and it was most sensitive to the location of housing where calves were fed and the body weight of the arriving calf. The mortality risk in the first 21 d after arrival (early) was calculated to be 2.2%, whereas the risk of mortality after 21 d (late) was 3.7%. The risks of early and late mortality were most sensitive to the level of dehydration measured at arrival and the season at arrival for the purchased calves, respectively. The calculated average daily gain was 1.12 kg/d and it was most sensitive to housing location. The breakeven purchase price was calculated to be $242.49 per calf, which was most sensitive to the housing location where the calves were fed. The results of this analysis demonstrate that veal producers need to consider many variables before purchasing calves. In addition to overall market conditions, veal producers should factor health characteristics and the expected performance of the calves they purchase into what they are willing to pay for them.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".