A case of tail-biting on a multi-site swine operation in Ontario.
Bibliographic record
Abstract
This case study describes a severe tail-biting event on a multi-site swine operation in Ontario and outlines the management strategies implemented in an attempt to control the problem. An established social order was clearly present before the tail-biting event occurred. Over 40% of tail-docked pigs in 3 of 8 grower-finisher barns were severely affected, leading to higher mortality and increased numbers of pigs re-housed in hospital pens. Environmental factors, management practices, and animal health in the barns experiencing the tail-biting event are described, including detection of the mycotoxin deoxynivalenol in corn at > 2 ppm. Changes implemented in response to tail-biting included altering the phase-feeding schedule, adding enrichment devices, and increasing surveillance. The subsequent cohort of pigs was followed through the finisher barns and did not engage in the same severity or prevalence of tail-biting as the previous cohort of pigs which experienced the tail-biting event. Key clinical message: No single factor was identified as the initiating cause for the severe tail-biting event. The subsequent cohort of pigs in 4 barns of the same operation were monitored for tail-biting from entry until market, and the incidence of tail-biting was very low.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".