The United States' prohibition of horsemeat for human consumption: is this a good law?
Bibliographic record
Abstract
On May 24, 2007, the last slaughterhouse in the USA producing horsemeat for human consumption was closed by State statute (1). Recently there have been several state and federal regulatory initiatives in the USA intended to prevent the slaughter of horses for human consumption (2,3). On January 27, 2007, simultaneous bills were introduced in the Senate and the House to prohibit the slaughter of horses for human consumption and to ban the transport of live horses from the United States to countries where they could be slaughtered for human consumption (2). The American Veterinary Medical Association (AVMA) and the American Association of Equine Practitioners (AAEP) are pursuing defeat of these amendments to the Horse Protection Act (2,4,5). The veterinary concerns about the slaughter prohibition are motivated by the future welfare risk for horses that otherwise would be removed from the population by slaughter. Some authors have suggested that the horse slaughter industry functions to remove and, therefore, protect old (mean age 11.4 y) and unsound working and riding horses from neglect or abuse (6–9). Low value animals have previously been identified as being at increased risk for neglect (10). Economic modeling suggests a horse slaughter ban would be equivalent to the immediate loss in value of $300.00 per horse in the USA at a cost of $50 million annually (11).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.028 |
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".