An Investigation of Associations Between Management and Feather Damage in Canadian Laying Hens Housed in Furnished Cages
Bibliographic record
Abstract
Feather pecking is a continuous welfare challenge in the housing of egg-laying hens. Canada is currently making the transition from conventional cages to alternative housing systems. However, feather damage (FD) among laying hens due to feather pecking remains a welfare concern. An explorative approach was taken to assess bird, housing, and management associations with FD in Canadian laying hens housed in alternative systems. A questionnaire focused on housing and management practices was administered to 122 laying farms across Canada in autumn of 2017 (response rate of 52.5%), yielding information on a subset of 26 flocks housed in furnished cages. Additionally, a three-point feather cover scoring system was developed to estimate the prevalence of FD. Farmers assessed FD by sampling 50 birds per flock. Linear regression modeling was applied to explain FD as a function of 6 variables (out of an available 54). Of the 6 modeled variables, "increased age", "brown feather colour", "midnight feeding", and "no scratch area" were associated with higher levels of FD at farm level (R² = 0.77). The results indicated that FD resulting from feather pecking is a multifactorial problem, and supported existing evidence that FD increases as birds age. These results also suggested that "feather colour", "midnight feeding", and "access to (or lack of) a scratch area or additional substrate" play a role in FD prevalence in furnished cages.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".