PSIII-8 A risk factor analysis of health traits in turkeys (Meleagris gallopavo) on Canadian farms
Bibliographic record
Abstract
Abstract Current production systems for commercial turkeys can lead to challenges including the development of footpad dermatitis (FPD) and aggressive pecking. Both have welfare and economic implications for turkey production. To date, there have been no epidemiological studies conducted in Canada on risk factors for either FPD or aggressive pecking. In this study, over 500 turkey farmers across Canada will receive a cross-sectional survey which includes a health-scoring guide and a questionnaire. Farmers will be asked to record the health status of 30 turkeys on their farms using the illustrated instructions to score head injuries, skin damage, and FPD. The information on head injuries and skin damage will provide insight into the prevalence of aggressive pecking within the flock. Farmers will score these areas on a three-point scale where a score of zero indicates no damage and two indicates severe damage. Additionally, an inventory of housing and management practices will be taken on each farm using a questionnaire covering topics on bird characteristics, lighting, air quality, litter quality, feeding, and health. The data obtained from this survey will be used to 1) estimate the prevalence of FPD and pecking injuries, 2) describe housing and management practices, 3) identify risk factors for FPD and pecking injuries and 4) make recommendations to reduce the prevalence of FPD and pecking injuries. With the information from this study we plan to develop a management tool tailored to the Canadian industry to reduce FPD and pecking injuries on Canadian farms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".