How might veterinarians do more for animal welfare? Comment les vétérinaires peuvent-ils faire plus pour améliorer le bien-être des animaux?
Bibliographic record
Abstract
This point reflects today’s holistic understanding of welfare as the state of the animal’s mind and body and the extent to which its nature is satisfied (3). If veterinarians are to do more for animal welfare, it is not enough for them to identify that herd productivity is down, that an animal is sick, or that the environment predisposes animals to illness. Even when an animal is healthy and the environment meets its physical needs, the environment should also promote mental welfare and enable the animal to satisfy its nature. For example, a horse may have a clean, spacious stall, but lack social contact; a hospitalized cat may have a clean cage, but have inadequate separation between its food and its litter-tray and have nowhere to hide (4). Veterinarians also need to be aware that clinical signs associated with compromised physical welfare may be associated with reduced mental and natural aspects of welfare. For example, dairy cows in tie-stalls may develop muscle cramps by the end of the winter due to lack of exercise. The same lack of exercise also frustrates expression of the bovine nature of moving about to graze and interact. Thus, an exercise yard is desirable to promote all 3 aspects of welfare, not only health. These examples of a more inclusive approach to veterinary assessment indicate how veterinarians might do more for animal welfare in the course of their clinical work.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.029 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".