Multiple trait selection for maternal productivity in beef cattle
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".