A Cross-Sectional Study on the Prevalence of Footpad Dermatitis in Canadian Turkeys
Bibliographic record
Abstract
Footpad dermatitis (FPD) can be a prevalent issue in commercial turkey production. This study aimed to identify the bird, housing, and management-related factors associated with the prevalence of FPD in the Canadian turkey flocks. A questionnaire and flock health scoring system were developed and disseminated to ~500 commercial turkey farmers across Canada. Farmers were asked to score FPD on a subset of 30 birds within their flock using a 0–2 scoring scale based on severity. The prevalence of FPD in the flock was calculated as the percentage of affected birds (score 1 or 2). A multivariate linear regression modeling was used to identify the factors associated with the prevalence of FPD. Four variables were included in the final model and accounted for 26.7% of the variation in FPD prevalence among the flocks. FPD prevalence was higher with increasing bird weight (3.6 ± 1.13), higher in flocks bedded with straw (12.1 ± 7.9), higher in flocks where birds were picked up less frequently during daily inspections (11.6 ± 8.10), and higher in flocks that used feed/water additives to reduce litter moisture (20.5 ± 10.59). These findings are a preliminary exploratory assessment of risk factors related to FPD prevalence on Canadian turkey farms. While these findings emphasize the importance of litter management and the stockperson, estimates and P -values from this study should be interpreted with caution. Further, longitudinal studies with the identified variables are required to better determine their influence on FPD.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".