Improving Prediction of National Evaluations by Use of Data from Other Countries
Bibliographic record
Abstract
National and international Holstein bull evaluations from Canada, France, Germany, Italy, The Netherlands, and the US were examined to determine whether inclusion of data from other countries increased the accuracy of prediction of national evaluations for milk, fat, and protein yields. The six national and six international evaluations from February 1995 were compared with national evaluations in January and February 1999. The later national evaluations were assumed to be improved estimates of true genetic merit because of added data. Correlations with later national evaluations generally were larger for earlier national evaluations than for international evaluations, probably because of the larger part-whole relationship between earlier and later national evaluations. However, standard deviations of difference of 1995 evaluations from later national evaluation were lower for international evaluations than for earlier national evaluations, which suggested improved prediction from inclusion of multinational data. For bulls with substantial increases in daughters, nationally and internationally, correlations were higher, and standard deviations of differences were lower for international evaluations compared with earlier national evaluations. Inclusion of multinational data improved the prediction of future national evaluations, especially for countries that import genetics of dairy cattle.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".