Bibliographic record
Abstract
The usefulness of multinational data for the improvement of national estimates of genetic merit of Holstein bulls was assessed. For 222 bulls, combined US-Canadian evaluations and evaluations from the US only from January 1993 for milk, fat, and protein yields were compared with their US only evaluations from August 1997. The correlations between the 1993 and 1997 evaluations and the standard deviations of differences in evaluations from added data favored the evaluations from the US only because of a partwhole relationship; often 1997 data were largely from US only data from 1993. However, the results for 35 bulls with reliability increases of >5% indicated that combining US and Canadian evaluations improved the prediction of future evaluations. The value of foreign data also was assessed from national and international evaluations on the scales of Canada, Germany, and the US. The changes from 1996 national evaluations to either 1996 international evaluations or 1997 national evaluati...
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".