Key animal welfare issues in commercially raised dairy calves: social environment, nutrition, and painful procedures
Bibliographic record
Abstract
Dairy calf welfare concerns are growing and new evidence suggests that the early life environment influences appropriate physical, behavioral, and cognitive development lasting into adulthood. This review highlights key evidence for the impacts of housing, diets, and painful procedures on calf welfare. We argue that these topics are currently critical welfare concerns, but are not the only points of concern. In addition to environmental requirements to maintain optimal health, dairy calves experience other challenges including social and nutritional restrictions. Individual housing is associated with impaired behavioral development and cognitive ability. Pair and group housing can mitigate some of these negative effects and should be encouraged. Restrictive milk allowances (<15% of body weight) lead to poor growth and hunger; these welfare concerns can be addressed with proper enhanced milk allowances and gradual weaning programs. Finally, dehorning is a critical animal welfare issue when pain control is withheld; calves show negative behavioral, physiological, and emotional responses during and after dehorning. The combined use of local anaesthetics and analgesics can mitigate these effects. An industry shift toward providing social companionship, enhanced milk allowances, and pain control during painful procedures would help to improve the welfare of dairy calves in intensive commercial rearing facilities.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".