Genetic effects of aging on egg production traits in the first laying cycle of White Leghorn strains and strain crosses
Bibliographic record
Abstract
Three White Leghorn strains, their two-way crosses, and two commercial lines were used to evaluate the effects of aging on heterosis (H), reciprocal effects, and additive (A), Z-chromosome (Z), and heterotic effects and their variances on egg quality traits during the first laying cycle. Egg weight (EW), specific gravity (SG), Haugh unit (HU), and albumen height (AH) were measured at 240, 350, and 450 d of age from hens housed one per cage in a randomized block design. The mean heterosis was significant over time only for EW. For EW, heterosis increased in magnitude with age. The mean heterosis for both HU and AH was also influenced by age. Reciprocal effects were significant, on average, across periods for all traits and were influenced by age. The age-related changes in additive, Z-chromosome, and heterotic effects varied significantly among strains, indicating differences by genetic group in response to aging for egg quality traits. The heterotic, environmental, and phenotypic variances increased with age for all traits, except for AH. The additive and Z-chromosome variances did not always increase with age. Their age trend varied, depending on the trait. Heritabilities decreased with advancing age, suggesting that selection to improve lifetime performance of egg quality traits can be done early in the cycle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".