0120 Mortality risk factors for calves entering a multilocation white veal farm in Ontario
Bibliographic record
Abstract
Mortality in preweaned dairy breed calves of both sexes represents a potential welfare issue and a source of economic loss for the industries involved. While morbidity and mortality in veal production has been described, this work reflects a wide range of management practices and requirements throughout the world. In preweaned dairy heifers, rates of morbidity and mortality can also range dramatically, due in large part to differing management strategies. It has been over two decades since mortality in veal calves in Ontario was last described. The objective of this retrospective cohort study was to determine if recorded on-arrival data collected from a large white veal farm could be used as predictors of mortality. Data was collected from 10,910 calves entering seven barns of a single white veal farm, all locations of barns within Ontario, from 1 Jan. to 31 Dec. 2014. Calves were followed until death or marketing; no calves were culled during the year. Three logistic regression models were used to determine the effects of weight on arrival, season of arrival, supplier, sex, barn, and standardized purchase price on the risk of overall mortality, mortality in the first 21 d after arrival, and mortality after the first 21 d. In the overall mortality model, significant associations (P < 0.05) were seen with season, barn, supplier and weight, with lighter weight calves arriving in winter being at increased odds of mortality. The early mortality model contained significant (P < 0.05) associations with weight, season, barn, supplier and tended (P < 0.10) to have an association with standardized price; lighter weight calves arriving in winter at lower prices were at increased odds of mortality. The late mortality model had significant (P < 0.05) associations with season of arrival, barn and supplier. While not a proxy for body condition, increased weight on arrival being protective for early mortality may have somewhat reflected this, as the distribution of weights was fairly tight and likely represented calves at a week of age or less. Although failure of passive transfer is a significant risk factor for mortality, the seasonal association we saw could reflect early life nutrition stress as opposed to seasonal variation in passive transfer. A further exploration of dairy farm of origin risk factors for veal calf mortality may serve to improve the productivity and welfare of dairy calves of both sexes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".