Effect of health status evaluated at arrival on growth in milk-fed veal calves: A prospective single cohort study
Bibliographic record
Abstract
The objective of this prospective single cohort study was to determine the effect of health status at arrival on growth in milk-fed veal calves. Upon arrival at the veal facility, calves were evaluated using a standardized health scoring system and weighed, and the supplier of the calf was recorded. The calves were followed until slaughter, when the hot carcass weight (HCW) was reported. To calculate average daily gain (ADG), the HCW was transformed into an estimated live weight, weight at arrival was subtracted, and this value was divided by the number of days on feed. A mixed linear regression model was created to evaluate the association of health status on arrival with the ADG throughout the production period. A total of 4,825 calves were evaluated at arrival; however, due to inconsistent HCW data from one slaughter plant, and 357 calves dying during the production period, 2,283 calves were used for analysis. In the final model, 7 variables were significantly associated with ADG. Housing location within the farm, method of calf procurement (drover or auction-derived calves versus direct delivery from local farms) and having a higher body weight at arrival were associated with a higher ADG. The season of arrival (summer or fall compared with winter) and being dehydrated at arrival were associated with a lower ADG. Days on feed was also significant in the multivariable model and had a quadratic relationship with ADG. The associations identified suggest that there may be value in scoring dehydration and body weight at arrival to a veal facility.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".