Bibliographic record
Abstract
Contributors.Preface..I. Theoretical Framework.1. The Ethical Imperative to Control Pain and Suffering in Farm Animals (Bernard E. Rollin).2. Human-Livestock Interaction (Paul H. Hemsweorth).3. Quality of Life for Farm Animals: Linking Science, Ethics, and Animal Welfare (David Fraser and Daniel M. Weary).4. Pain in Farm Animals: Nature, Recognition, and Management (G. John Benson).5. A Concept of Welfare Based on Feelings (Ian J. H. Duncan).6. Meeting Physical Needs: Environmental Management of Well-Being (Ted H. Friend).7. Principles for Handing Grazing Animals (Temple Grandin).8. Principles for the Design of Handling Facilities and Transport Systems (Temple Grandin).II. Practical Applications.10. Production Practice sand Well-Being: Beef Cattle (Joseph M. Stookey and Jon M. Watts).11. Animal Well-Being in the U. S. Dairy Industry (Franklyn B. Garry).12. Production Practices and Well-Being Swine.(Timothy E. Blackwell).13. Maximizing Well-Being and Minimizing Pain and Suffering: Sheep (Cleon V. Kimberling and Gerilyn A. Parsons).14. Welfare Problems of Poultry (Ian J. H. Duncan).15. Rethinking Painful Management Practices (Daniel M. Wary and David Fraser).16. Alternatives to Conventional Livestock Production Methods (Michael C. Appleby).17. Euthanasia (Robert E. Meyer and W. E. Morgan Morrow).Appendix: U. S. and Canadian Veterinary Medical Associations Positions on Food Animals.Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".