Bibliographic record
Abstract
Genomic variances have been estimated and used in GMACE since 2011, to adjust for differences among countries in the scaling of young bull genomic evaluations relative to progeny-tested bulls. Interbull has implemented validation tests for national genomic evaluations, which countries must pass in order to participate in GMACE, and the sharing of data and knowledge among countries for genomic evaluations has also increased. Each of these factors can improve consistency of genomic results among countries, and may reduce the need for genomic variance adjustments in GMACE. Cross-validation tests have been used previously to compare GMACE results when using versus not using genomic variance adjustments, and have shown clear advantages for including genomic variance adjustments. When repeated on current data for the present study, however, the cross-validation results no longer showed this clear advantage. Genomic variance adjustments were helpful for some traits and countries but not for others. On balance across all traits and countries, there was no longer a clear advantage either way. The international sharing of data and knowledge, combined with genomic validation tests of Interbull are likely helping to reduce differences among countries in the relative scaling of genomic versus progeny-test evaluations within the same 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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.089 | 0.035 |
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".