Bibliographic record
Abstract
National genomic evaluations of young bulls (GEBV) are combined by Interbull, using an international genomic MACE model (GMACE), with non-zero residual correlations to account for sharing of genotypes among national genomic evaluation systems. It was observed recently that GMACE results for mastitis resistance were inconsistent with corresponding results for somatic cell score. This study examined the current GMACE methods, and new modifications to better account for different heritabilities and for different genomic reliabilities among countries for a given trait. A parameter space was defined that bounds GMACE results, on the scale of each country, to fall somewhere between the national GEBV, and predictions of international GEBV when sharing of genotypes, common SNP panels, etc, are ignored. Distances to either boundary were estimated as a function of the degree of data sharing observed among national genomic evaluation systems. The proposed modifications to GMACE had largest effects on traits with a wide range of heritabilities among countries, such as mastitis resistance, and on the scales of countries that had relatively low national genomic reliabilities. Results from GMACE were much more consistent between mastitis and somatic cell score after the modifications, and also among all other traits evaluated by Interbull.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".