Rearing laying hens: Early environmental complexity and genetic strain have life-long effects on keel bone size and fractures
Bibliographic record
Abstract
Keel bone damage (KBD) is a major welfare concern for laying hens. Environmental complexity during rearing is suggested to promote skeletal development and reduce KB fractures (KBF). We investigated the effect of rearing environment and genetic strain on KB development and health. Three consecutive replicate flocks of brown (B) and white-feathered (W) layer chicks (3000/ flock) were raised in three styles of rearing aviaries of Low,Mid, or High complexity and in conventional cages (Conv). Pullets from flocks 1 and 3 (1080/ flock) were subsequently housed in 24 furnished cages of two sizes (small: 30 vs large: 60 birds) for lay. Samples of pullets and hens were assessed as follows: Dissections at six, 11, and 16 WoA in flocks 1 and 2 for KB size, radiographs at 30, 50, and 70 WoA in flocks 1 and 3, for KBD and dissections at 70 WoA in flock 3 for KBD. Rearing complexity affected KB size of pullets (Conv < High < Mid, Low; P< 0.0001). W pullets had larger keels than B when adjusted for body weight throughout rearing (strain x age P= 0.005) and at 70 WoA (P< 0.001). KBF prevalence at end-of-lay was affected by rearing complexity (High < Low, Conv, P= 0.002). During lay, KBF severity was affected by rearing complexity (High < Low, Mid; P= 0.007), strain (W < B; P< 0.0001) and age (30 < 50 < 70 WoA; P< 0.0001). At 70 WoA, an interaction of rearing by strain affected KBF severity (most severe: B-Conv, least severe: B-Mid and W-High, P= 0.003). KBF severity scores from radiographs and dissections corresponded moderately (r= 0.547, P< 0.0001). The severity of KB deviations was affected by the size of the furnished cages (large > small, P= 0.049). In conclusion, rearing complexity and strain affected KB size and fracture severity and prevalence. These data confirm that complex rearing aviaries that are known to promote exercise reduce KBF severity and prevalence throughout lay.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".