International dairy sire evaluation based on multiple-trait BLUP analysis of daughter performance records
Bibliographic record
Abstract
Our objective was to estimate variance components and predict sire EBV for milk, fat, and protein yield using a multiple-trait model in which lactation yield in each country was a different trait. Data included first lactation records of 16,145,832 Holstein-sired cows that calved between January 1, 1990 and December 31, 1997 in 243,466 herds in seventeen leading dairy countries. Milk, fat, and protein were analyzed separately using a 17-trait sire model; “traits” refer to measurements of the same biological parameter in different production systems. Our genetic model included effects of herd-year-season of calving, age at calving, milking frequency, and heterosis class (i.e., breed composition). Heritability estimates ranged from 0.24 in Australia (protein) to 0.34 in Israel (milk) and Netherlands (fat). Genetic correlations between countries ranged from 0.77 for Austria-Czech Republic (protein), Estonia-Finland (fat), Estonia-Ireland (milk), Estonia-Israel (milk), and Hungary-New Zealand (fat), to 0.96 for Australia-Ireland (milk), Australia-New Zealand (milk), Belgium-Netherlands (milk), and Belgium-USA (fat). Correlations differed from parameters used currently in international sire evaluations. In particular, genetic correlations were 0.91 – 0.96 between Australia, Ireland, and New Zealand; these countries rely heavily on rotational grazing. Correlations were also 0.91 – 0.96 between Belgium, Canada, Italy, Netherlands, and USA; these countries use intensive management systems. Correlations between these groups of countries were 0.80 – 0.90. The percent of elite bulls selected in common by each pair of countries ranged from 0.42 for Germany-Estonia and Germany-Israel to 0.78 for Belgium-Netherlands. _______________________________________________________________________________________________
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".