Genetic evaluation of carcass traits in dairy cattle
Bibliographic record
Abstract
Two new genetic evaluations on carcass data, extracted from the Normabev database, have been developed by the UMT 3G (Gestion Génétique et Génomique des populations bovines). The first (VB) is a multibreed evaluation of sires on their ability to produce veal calves. It evaluates sires of dual-purpose breeds Montbeliarde (MON) or Normande (NOR) and sires of beef breeds mated with dairy cows. The second (JB) evaluates sires of the MON, NOR and Simmental (SIM) breeds on their ability to produce young bulls for slaughter. The JB evaluation considers three traits simultaneously (carcass weight, age at slaughter and carcass conformation), the VB four (the same three, plus meat color). Heritability estimates are moderate to quite high for carcass weight and conformation (0.12 to 0.37, depending on the breed and the evaluation), lower for age at slaughter and meat color (0.05 to 0.27). Genetic correlations between carcass weight and conformation and between carcass weight and age at slaughter are favorable. Genetic correlations between JB traits on one hand and VB, milk production and female type traits on the other hand were also estimated. These correlations are low to moderate (favorable) between JB and milk production. Hence, meat production traits can be improved without compromising genetic trends on milk production traits too much. JB traits are favorably correlated with VB (0.32 to 0.70) and female type traits. \nVB polygenic evaluations became official in 2015. JB polygenic evaluations will be official in 2017. JB and VB genomic evaluations are currently developed in MON and NOR, following the methodology applied in other dairy cattle genomic evaluations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".