Invited review: The welfare of dairy cattle—Key concepts and the role of science
Bibliographic record
Abstract
Concerns about the welfare of animals typically include 3 questions: is the animal functioning well (e.g., good health, productivity, etc.), is the animal feeling well (e.g., absence of pain, etc.), and is the animal able to live according to its nature (e.g., perform natural behaviors that are thought to be important to it, such as grazing)? We review examples, primarily from our own research, showing how all 3 questions can be addressed using science. For example, we review work showing 1) how common diseases such as lameness can be better identified and prevented through improvements in the ways cows are housed and managed, 2) how pain caused by dehorning of dairy calves can be reduced, and 3) how environmental conditions affect cow preferences for indoor housing versus pasture. Disagreements about animal welfare can occur when different measures are used. For example, management systems that favor production may restrict natural behavior or can even lead to higher rates of disease. The best approaches are those that address all 3 types of concerns, for example, feeding systems for calves that allow expression of key behaviors (i.e., sucking on a teat), that avoid negative affect (i.e., hunger), and that allow for improved functioning (i.e., higher rates of body weight gain, and ultimately higher milk production).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".