Estimation of genetic parameters for pre-weaning growth traits in Dorper sheep under local Chinese conditions
Bibliographic record
Abstract
OBJECTIVE: This study aimed to estimate non-genetic factors, variance components, and genetic parameters, including heritability, genetic/phenotypic correlations for birth weight (BW), weaning weight (WW), average daily gain (ADG), and Kleiber ratio (KR) traits of Dorper sheep under localized Chinese conditions. METHODS: Data from 2,022 Dorper sheep lambs, collected between 2019 and 2021 at Inner Mongolia Sano Sheep Breeding Co., Ltd. were analyzed. Traits included BW, WW adjusted to 90 days, ADG, and KR. Generalized linear model (R 4.3.1) assessed non-genetic factors, including recipient dam age, sex, birth year, month, and herd. Six animal models were evaluated using ASReml's AIREML to determine the most suitable model for estimating genetic parameters while bivariate models were utilized to analyze genetic and phenotypic correlations. RESULTS: Recipient dam age, sex, birth year, month, and herd significantly affected all traits (p<0.05). Model 2, which incorporates direct additive genetic and maternal permanent environmental effects, was determined to be optimal. Heritability was low (BW: 0.0215; WW: 0.0287; ADG: 0.0391; KR: 0.0504). BW showed a negative genetic correlation with WW, ADG, and KR. In contrast, WW showed a strong positive genetic correlation with ADG (0.9952) and KR (0.9984), along with high phenotypic correlations with these traits (0.9829 and 0.8819, respectively). CONCLUSION: The low heritability limits direct selection for pre-weaning traits. Prioritizing WW enhances indirect genetic gains for ADG and KR, facilitating the optimization of Dorper sheep breeding strategies under Chinese intensive systems.
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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 | 0.000 |
| 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) | 0.000 | 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 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".