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Record W2099332789 · doi:10.1093/ps/79.3.296

Genetic effects of aging on egg production traits in the first laying cycle of White Leghorn strains and strain crosses

2000· article· en· W2099332789 on OpenAlexaff
Mônica Corrêa Ledur, R. W. Fairfull, I. McMillan, L. Asseltine

Bibliographic record

VenuePoultry Science · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHeterosisBiologyHeritabilityReciprocal crossAnimal scienceAnalysis of varianceRandomized block designAdditive genetic effectsStrain (injury)GeneticsHybridHorticultureAnatomyStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.229
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2000
Admission routes1
Has abstractyes

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