Litter size at lambing influences genetic evaluation of maternal rearing ability
Bibliographic record
Abstract
The genetic parameters for maternal rearing-ability of ewes were investigated by ignoring or defining the rearing ability trait separately by litter-size class (singles versus multiples) using multi-generational data from three, fully pedigreed Merino flocks differing in prolificacy, production level and environment. Genetic correlations (ra) between litter size (LSIZE) and the percentage of lambs surviving (PLSURV) were more negative with an increasing mean flock litter size (ra: –0.21 ± 0.17 to –0.73 ± 0.08), while the corresponding range in phenotypic correlations (rp) was substantially lower (rp: –0.15 ± 0.01 to –0.33 ± 0.01). Rearing-ability traits defined separately by litter-size class were highly correlated (ra: 0.49 ± 0.19 to 0.64 ± 0.38), but not genetically identical traits in the most prolific flock. Defining rearing-ability traits separately by litter-size class reduced the antagonistic genetic correlations between LSIZE and PLSURV to between –0.39 ± 0.14 and 0.14 ± 0.28, through accommodating the change in mean and variance of PLSURV with LSIZE. Similarly, linear transformation (TSURV) of PLSURV within each litter size to a common mean and variance reduced the antagonistic trend in genetic correlations between LSIZE and TSURV to range between –0.05 ± 0.17 and –0.43 ± 0.13. Since genetic correlations are low to moderate between TSURV and LSIZE, it is possible to select for improvements in both the number of lambs born and lamb survival simultaneously. Defining rearing-ability traits separately by litter-size class and the role of transformation will be investigated further for improving the accuracy of genetic evaluation for rearing ability across a range of flocks, breeds and environmental conditions using more extensive industry data.
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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) | 0.001 | 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".