Farmers' Perceptions About Health and Welfare Issues in Turkey Production
Bibliographic record
Abstract
Farmers play an essential role in the management of animals and ensuring their health and welfare. However, relatively little is known about the health and welfare-related issues farmers themselves find important in the turkey sector. As part of a larger study, a cross-sectional survey of turkey farmers was conducted in Canada to identify the main perceived reasons for culling, mortality, and carcass condemnations in their flocks. Additionally, farmers were asked to rate the importance of different health and welfare-related issues (i.e., mortality, aggressive pecking, disease, leg injuries, leg deformities, breast injuries, and varying body size) during their summer and winter production, as well as for the sector as a whole. A total of 83 responses were analyzed (response rate 20%). The most frequently mentioned reasons for the culling of turkeys included leg-related issues (90.0%), sickness (60.5%), and small body size (58.0%). The perceived reasons for mortality were most often unknown (59.7%), or related to cannibalism (41.6%) or dehydration (42.9%). The main reasons for carcass condemnations at processing were related to skin (33.8%) or subcutaneous conditions (64.7%). Leg deformities and mortality were considered the biggest issues for the turkey production sector. In general, farmers rated items as more of an issue when the question pertained to the sector as a whole rather than to their farm. These results increase our understanding of the health and welfare-related problems in turkey production that farmers find important. This can ultimately help focus research efforts in addressing these issues through improved management adaptations or breeding approaches, thereby improving both the well-being of farmers and birds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".