Estimation of genetic parameters and selection response for reproductive and growth traits in Rideau-Arcott sheep
Bibliographic record
Abstract
The goal of this study was to estimate genetic parameters and predict direct and correlated response to selection for lamb growth traits and ewe reproductive traits, based on single-trait selection or combining multiple traits in an optimum index that targets total litter post-weaning weight in the first lambing as the main selection goal. Heritability estimates ranged from 0.04 to 0.19. Genetic correlations between growth and reproductive traits ranged from −0.24 to 0.15. The indirect response to selection for reproductive traits in later lambings, by selecting on first lambing performance, was 11%–25% greater than direct selection. The response to indirect selection for composite reproductive traits, i.e., total weaning weight or total post-weaning weight, by selecting on individual lamb weaning weight or post-weaning weight was 1%–69% greater than direct selection, but it was accompanied by a negative response on litter size. However, combining alternate growth and reproductive traits in optimum selection index resulted in correlated response of up to 96% greater than direct selection response for reproductive traits without a negative response on litter size. Therefore, multiple trait selection using an index of component traits was more effective than direct selection for a composite trait.
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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 | 0.001 | 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".