Bibliographic record
Abstract
With genotype exchanges between countries genomic evaluations have to be based on phenotypic information from international conventional MACE evaluation. As dependent variable of genomic model, bulls’ deregressed MACE EBV are usually used in SNP effect or DGV estimation. Corresponding to the deregressed proofs, EDC or daughter reliability contributed by all domestic and foreign daughters need to calculated as well. For routine prediction of GEBV of young candidate animals, parental average or male pedigree index and their associated reliabilities need to be calculated using the most recent conventional MACE evaluation. At the Interbull Technical Workshop on Genomics held in Guelph, Canada, March 2011, a group of animal geneticists discussed on the use of MACE results as input for genomic models. The group focused on four main questions, which were then discussed in a following plenary discussion. Different statistical methods have been applied by countries to obtain the deregressed MACE proofs and their reliabilities or EDC. All member countries and Interbull centre were encouraged to exchange their experience, statistical procedures, and computer software to make the best use of conventional MACE evaluation results for own genomic prediction.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".