Mortality risk factors for calves entering a multi-location white veal farm in Ontario, Canada
Bibliographic record
Abstract
Mortality in preweaned dairy-breed calves, whether they are replacement dairy heifers, veal animals, or dairy beef animals, represents both a welfare issue and a source of economic loss for the industries involved. Studies describing morbidity and mortality in veal calves have illustrated different management practices and requirements in terms of housing and nutrition around the world. Studies examining the rearing of replacement dairy heifers have shown that rates of morbidity and mortality can vary dramatically between farms, perhaps reflecting differences in management strategies. It has been over 2 decades since morbidity and mortality in veal calves in Ontario were described. The objective of this retrospective population cohort study was to describe mortality and determine whether on-arrival information could be used to predict mortality risk. Predictors could be used to both better classify and group calves on arrival and provide feedback to suppliers about the characteristics of the highest- and lowest-risk calves. We collected data from 10,910 calves entering 7 barns of a single white veal farm, all in Ontario, from January 1 to December 31, 2014. Calves were followed until death or marketing (typically 140 to 150 d). We developed logistic regression models to determine the effects of weight on arrival, season of arrival, supplier, sex, barn, and purchase price on the risk of total mortality, early mortality (0-21d after arrival), and late mortality (>21d after arrival). We identified significant associations between season, barn, supplier, weight, and total mortality risk, with lighter-weight calves arriving in winter being at increased risk. Early mortality was significantly associated with weight, season, barn, and supplier, and tended to be associated with standardized price; lighter-weight calves arriving in winter at lower prices were at increased risk. Late mortality was significantly associated with season of arrival, barn, and supplier. On-arrival measures better predicted early mortality compared with late or total mortality. A further exploration of risk factors from the dairy farm of origin for veal calf mortality would serve to improve the productivity and welfare of calves of both sexes born on dairy farms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".