Herd-level risk factors associated with preweaning mortality on Ontario dairy farms
Bibliographic record
Abstract
The objective of this cross-sectional, herd-level study was to identify associations between calf management practices and herd-level preweaning mortality on Ontario dairy farms. From April to August 2022, a convenience sample of 100 dairy farms from Ontario, Canada, were visited once. A questionnaire, covering farm biosecurity, calving, colostrum management, preweaning nutrition, and housing, was verbally administered at each farm visit. Furthermore, data regarding preweaning calf mortality were retrieved from each farm's herd management software or records, including the total count of calves that survived, were culled, or died during the preweaning period for the 365 d before the farm visit. Preweaning mortality was defined as the proportion of calves that died between ≥48 h and 60 d of age divided by the total number of calves alive after 48 h of age. The herd-level preweaning mortality risk on sampled farms ranged from 0 to 15.9%, with an average of 2.8% (SD = 3.8%). A multivariable Poisson regression model was used to evaluate associations between 22 explanatory variables and preweaning mortality. Factors associated with greater herd-level preweaning mortality were larger herd size; having treatment protocols for diarrhea, pneumonia or navel infection written in collaboration with a veterinarian (compared with farms with the same treatment protocols developed without a veterinarian), and the herd veterinarian never inquiring about calf health (compared with farms where the herd veterinarian inquired sometimes). Factors associated with lower herd-level preweaning mortality were using the calving pen for sick cows, having more than 4 people working with calves, offering calves a minimum volume of ≥9 L of milk per day, and farmers with a level of formal education higher than secondary school. These results indicate that producers may be able to reduce preweaning calf mortality by providing adequate labor for calf care, offering calves sufficient volumes of milk, being proactive in communicating with their veterinary practitioners about calf health, and potentially by engaging in continuous education.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".