A survey of dairy calf management practices in Canada that affect animal welfare
Bibliographic record
Abstract
There is growing interest among the public in farm animal welfare and a need for methods to assess animal welfare on farm. A survey on calf rearing practices that might affect dairy calf welfare was performed via a 1-h interview on 115 dairy farms (mean +/- SD: herd size=52.5+/-20.9 cows; milk production=8,697+/-1,153L) distributed throughout the province of Quebec. Despite frequent recommendations, many dairy producers continue to use management practices that increase the health risks of milk-fed calves. Major risk factors for poor calf welfare identified were 1) no use of calving pen in 51.3% of herds and low level of surveillance of calvings, especially at nighttime (once every 12h), 2) no disinfection of newborn's navel in 36.8% of herds, and delayed identification and, hence, calf monitoring (3 d), 3) 15.6% of farms relied on the dam to provide colostrum and none checked colostrum quality or passive transfer of immunity, 4) dehorning and removal of extra teats proceeded at late ages (6.4 wk and 6.7 mo, respectively) and without adequate pain control, 5) use of traditional restrictive milk feeding and waste milk distributed to unweaned calves without precaution in 48.2% of herds, 6) abrupt weaning performed in 16.5% of herds, and 7) calves housed individually in 87.9% of herds, and most inappropriate housing systems (crate=27.0%, tie-stall=13.9%, attached against a wall=5.7%) remained. This risk factor assessment was the first step in an intervention strategy to improve calf welfare on dairy farms.
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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 | 0.001 | 0.002 |
| 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".