Preliminary report from interbull task force on the role of genomic information in genetic evaluations
Bibliographic record
Abstract
G. Banos, M. Calus, V. Ducrocq, J. Durr, H. Jorjani, Z. Liu, E. Mantysaari, P. Sullivan and P. VanRaden a Faculty of Veterinary Medicine, Aristole University of Thessaloniki, Box 393 GR-54124 Thessaloniki, Greece Animal Breeding and Genomics Centre, Animal Sciences Group, Wageningen University and Research Centre, 8200 AB Lelystad, The Netherlands Station de Genetique Quantitative et Appliquee, INRA, Domaine de Vilvert, 78352 Jouy-en-Josas, France d Interbull Centre, Department of Animal Breeding and Genetics, SLU, Box 7023 – 750 07, Uppsala Sweden VIT, Heideweg 1, D-27283 Verden, Germany f Agrifood Research Finland, Animal Production, 31600 Jokioinen, Finland g Canadian Dairy Network,150 Research Lane, Suite 307, Guelph, N1G4T2, Ontario, Canada h USDA – ARS, Animal Improvement Programs Laboratory, Building 5, BARC-West, Beltsville, Maryland 20705, USA
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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 | 0.092 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".