Bibliographic record
Abstract
Reliabilities of genomic GEBV are approximated nationally by country, and via GMACE at the international level. In previous studies, GMACE reliabilities were sometimes lower than expected, relative to corresponding national values. Reasons for misalignment were investigated in the present study, which revealed that two important data contributions were being ignored for the GMACE reliabilities; the effective daughter contributions (EDC) of a bull's maternal grand-sire, and the contribution of cow records for the dam. The GMACE system was updated to properly incorporate maternal grand-sire EDC for both reliability approximation and for genomic variance estimation. The information from cow records for the dam was added only for reliability approximation, and only if it was helpful to align approximate reliabilities, since records of the dam are otherwise excluded from the GMACE model. These updates improved alignment of GMACE reliabilities with national values. With only a few exceptions, GMACE reliabilities became consistently equal or higher than national values, which is the generally expected pattern of alignment.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".