Extent and Benefits of Multi-Country Progeny Testing of Young Dairy Sires
Bibliographic record
Abstract
One of the current trends within the artificial insemination industry is to progeny test young dairy bulls in multiple countries. The objectives of this study were to assess the extent of multi-country progeny testing and to measure the corresponding gains in reliability of international breeding value estimates. Data of Holstein bulls that were born between July 1, 1992, and December 31, 1994, and progeny tested in countries that participate in the International Bull Evaluation Service were used in the present study, because these were the youngest bulls that had completed multi-country progeny testing before the study. Based on August 1999 international sire evaluation data, a total of 562 bulls from 10 countries were multi-country sampled for production traits during this 2.5-yr period, and 233 bulls from seven countries were multi-country sampled for type traits. The United States, Canada, The Netherlands, France, and Germany were most active in multicountry progeny testing, and Germany, New Zealand, Australia, France, and The Netherlands were the most common countries of foreign sampling. Mean reliabilities of international breeding values were calculated within each country. Means for milk yield were 0.89 for single-country sampled bulls with local progeny (i.e., progeny in the home country), 0.71 for single-country sampled bulls with no local progeny, 0.90 for multicountry sampled bulls with local progeny, and 0.78 for multi-country sampled bulls with no local progeny. Mean reliabilities for teat placement for these groups of bulls were 0.80, 0.71, 0.88, and 0.83, respectively, and means for rear udder width were 0.79, 0.60, 0.85, and 0.68, respectively. Gains in reliability in the country of foreign sampling were greatest when foreign progeny were located in countries that had low genetic correlations with the home country.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".