Retrospective Analysis of the Accuracy of Conversion Equations and Multiple-Trait, Across-Country Evaluations of Holstein Bulls used Internationally
Bibliographic record
Abstract
In 1995, the multiple-trait across country genetic evaluation procedure replaced regression-based conversion equations as the preferred method for international genetic comparisons of dairy bulls. In the present study, February 1999 estimated breeding values of 632 foreign Holstein bulls that were used in Canada, Germany, Italy, The Netherlands, Sweden, and the US were compared with January 1995 predictions from home country data only. January 1995 predicted breeding values for each importing country were calculated using three methods: the multiple-trait, across-country evaluation procedure; conversion equations based on the multiple-trait, across-country evaluations; and conversion equations based on the Wilmink method. Mean correlations between 1999 estimated breeding values in the importing countries and 1995 predictions from international data were from 0.76 to 0.81 for all methods. The multiple-trait, across-country evaluation procedure is expected to lead to selection of different bulls, because bulls were allowed to be ranked differently in each country, but no significant increase in accuracy of selection was observed. The lack of improvement in accuracy of prediction was most likely due to limitations in data structure. International genetic comparisons are largely driven by data from a relatively small number of evaluated bulls with exported semen. Data from siblings and more distant relatives provide only weak, indirect genetic links between countries, and inclusion of such data seems to provide a minimal improvement in accuracy. Limitations in data structure might be alleviated by methods that define environments by climate or management factors rather than country borders.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".