Animal Welfare: An Aspect of Care, Sustainability, and Food Quality Required by the Public
Bibliographic record
Abstract
People feel that they have obligations to the animals that they use and show some degree of care behavior toward them. In addition, animal welfare is an aspect of our decisions about whether animal-usage systems are sustainable. A system that results in poor welfare is unsustainable because it is unacceptable to many people. The quality of animal products is now judged in relation to the ethics of production, including impact on the animal's welfare on immediate features and on consequences for consumers. Because genetic selection and management for high productivity may lead to more disease and other aspects of poor welfare, consumers demand some major changes in animal-production systems. In teaching animal welfare, a clear definition that can be related to other concepts such as needs, health, and stress is needed. The methodology for the scientific assessment of animal welfare has developed rapidly in recent years and has become a major scientific discipline. No veterinary degree course should be approved unless a full course on the science of animal welfare and relevant aspects of ethics and law have been taught. Each country should have a national advisory committee on animal-welfare science, made up of independent scientists, including veterinarians, who can write impartial reviews of the state of scientific knowledge.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".