Multiple-Trait Multiple-Country Genetic Evaluations for Udder Health Traits
Bibliographic record
Abstract
International genetic bull evaluations of somatic cell counts (SCC) from 8 different Holstein populations and clinical mastitis from 3 of these populations were inferred simultaneously using a multiple-trait-multiple-country evaluation (MT-MACE) model. This model considered effective independent weighting factors and multivariately deregressed national genetic evaluations for countries with multiple-trait national models. Predictions of genetic merit from MT-MACE and their reliabilities were compared with the corresponding results from 2 separate single-trait-multiple-country evaluations (ST-MACE) for different groups of bulls. The assumed heritabilities for clinical mastitis (h(2) = 0.02 to 0.05) were substantially lower than the heritabilities for SCC (h(2) = 0.08 to 0.27). The predictive ability of MT-MACE was essentially equal to or better than the predictive ability of ST-MACE for all country-trait combinations, but both methods yielded effectively unbiased and consistent consecutive predictions (correlation > 0.93). Both sets of predictions also agreed well with future national genetic evaluations for bulls receiving additional daughter information (correlation > 0.96), except for evaluations for which within-country correlations were utilized internationally, but not nationally (correlation = 0.86 to 0.97). The reliabilities for MT-MACE were essentially equal to or higher than reliabilities for ST-MACE, depending on the trait and group of bulls in question. Reliabilities increased most for young bulls, and for clinical mastitis in countries that did not use the within-country correlations with SCC in the national evaluation (up to a 23% increase in average reliability).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".