Transport Fitness of Cull Sows and Boars: A Comparison of Different Guidelines on Fitness for Transport
Bibliographic record
Abstract
Sows and boars that have reached the end of their productive lives have a greater risk for welfare problems. This paper reviews literature on culling reasons that may affect the animals’ fitness for transport. The top two reasons identified for culling boars were: obesity and reproductive problems. Sows are most often culled due to lameness, low body condition, or failure to rebreed. The OIE (World Organization for Animal Health) fitness for transport guidelines that would apply to sows and boars were compared with documents from the Canadian Code of Practice, Northern American Meat Institute (NAMI), EU-UK-DEFRA (European Union-United Kingdom, Dept. Environment, Food and Rural Affairs), U.S. National Pork Board, European Practical Guidelines to Assess Fitness for Transport of Pigs, and U.S. Pork Trucker Quality Assurance. The guidelines had the greatest agreement on the following fitness for transport issues: non-ambulatory, severely injured animals, sows in the last ten percent of pregnancy and sows with uterine prolapses were not fit for transport. There was less agreement on low body condition. One of the reasons for the lack of agreement is that there were stakeholders who specialized in transporting and processing extremely thin animals. A standard that would severely restrict the transport and slaughter of these animals could hinder the business practices of these stakeholders. Many welfare specialists would agree that some of these animals would be unfit for transport.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".