Impact of Genetic Correlations on Accuracy of Predicting Future Evaluations
Bibliographic record
Abstract
International evaluations for Holstein bulls were calculated by the International Bull Evaluation Service with data available in February 1995 from Canada, Denmark, France, Germany, Italy, Sweden, The Netherlands, and the US using current methodology with either correlations between countries of unity (0.995) or estimates of less than unity.To determine which of the two evaluation methods was most accurate, results of the two sets of evaluations for milk, fat, and protein yields for each country were compared with national evaluations in 1999.The 1999 national evaluations were assumed to be the best estimates of true genetic merit on a particular national scale.To reduce the impact of the part-whole relationship that results from earlier national data, a key part of the study was restricted to bulls with data from at least twice as many daughters for 1999 national evaluation as for 1995.Correlations and standard deviations of differences from later national evaluations showed no advantage to accounting for genetic correlations between countries.For bulls without national data in the earlier international evaluations and, therefore, no data in common with the later national evaluation, the advantage from using variable genetic correlations for yield was small.Thus, the use of variable genetic correlations had marginal value.
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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 | 0.076 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".