Multiple trait selection for maternal productivity in beef cattle
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Bibliographic record
Abstract
Summary The aim of this study was to develop a multiple trait genetic evaluation and selection tool for maternal productivity in beef cattle, particularly in the Hereford breed. Component traits of the maternal productivity index (MPI) were chosen on the basis of their potential to contribute to consistently weaning heavy calves over a sustained herd life, while controlling cow maintenance costs. (Co)variance components were estimated with a multiple trait model including direct and maternal birth weight, direct and maternal weaning weight, weight of the cow at the time her calf is weaned and survival, defined as the ability of a female to produce at least three calves given she became a dam. Although direct and maternal birth weight were included in the (co)variance parameters model, these traits were not included in the index. Estimates of heritability were 0.19, 0.18, 0.50 and 0.07 for direct and maternal weaning weight, cow weight, and survival, respectively. The correlation between direct and maternal components of weaning weight was −0.42. The genetic correlation estimated between direct weaning weight and cow weight was 0.85, while a low genetic correlation of −0.17 was estimated between maternal weaning weight and cow weight. Survival had a near zero (−0.01) genetic correlation with maternal weaning weight, but negative genetic correlations with direct weaning weight (−0.52) and cow weight (−0.48). The MPI was constructed as a linear function of derived economic weights multiplied by estimated breeding values for the four component traits from the model. Estimated economic values were $3.00, $2.70, $−0.49 and $2.39 for direct and maternal weaning weight, cow weight, and survival (expressed as a percentage), respectively. Relative economic weights were 0.30, 0.26, 0.17 and 0.27 for direct and maternal weaning weight, cow weight, and survival, respectively. A simulation study indicated that positive genetic trend would be expected in all component traits although increases in cow weight would be moderate.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it