Estimation of Genetic Parametersand Evaluation of Sires for Growth and Fleece Yield Traits Using Animal Model in Chokla Sheep
Bibliographic record
Abstract
The data on 1 214 animals progeny of 110 sires of Chokla sheep maintained at CSWRI, Avikanagar, Rajasthan were used in the present study considering the traits birth weight, weaning weight, 6 months weight and first greasy fleece yield. The least squares means were (2.81±0.02) kg, (12.12±0.11) kg, (16.91±0.13) kg and (0.95±0.01) kg under model 2 and (2.82±0.02) kg, (11.87±0.10) kg, (16.86±0.12) kg and (0.96±0.01) kg, respectively, under model 8 for birth, weaning and 6 month weight and first greasy fleece yield. Multivariate REML analysis has estimated slightly higher coefficient of variation than the univariate and model 8 analyses. The fixed effect of year had highly significant (P<0.01) effect on all the traits studies under model 2 and model 8 analysis. The differences in body weight traits at birth, weaning and at 6 month of age due to sex were highly significant (P<0.01). On first greasy fleece weight, sex had significant (P<0.05) effect under model 8. Male lambs were heavier than female lambs at all ages and produced more wool than females. The sire effect accounted for more variation under model 2 than model 8 for all traits except birth weight. The coefficients of multiple determination under model 8 were, 13.32%, 30.80%, 33.52% and 26.11%, respectively, for birth, weaning, 6 month and first greasy fleece weights. The higher variation at 6 month weight suggests applying intense selection pressure at the age of 6 month. The sires have evaluated and ranked on the basis of solutions obtained through univariate and multivariate REML using animal model and BLUP value for sire effects under model 8. The superiority of the best sires were around 5% for the body weight traits under BLUP1 but above 17% under BLUP2 and BLUP3. In BLUP2 and BLUP3, more than 64% animals were superior to the population mean for body weight traits. The superiority of the best animals (as per cent of the raw mean) was above 19% under BLUP2 and BLUP3 for body weight traits but this value was 18.21% (BLUP2) and 15.18% (BLUP3) for first greasy fleece yield. From these results, it was observed that the REML using animal model could be used to evaluate the animals along with their sires and dams.
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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 | 0.002 |
| 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) | 0.001 | 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".