Bibliographic record
Abstract
Interbull has been responsible for comparing dairy bulls across countries since the mid-1980s. The current methodology is called MACE (multiple across country evaluations) which has been in use since 1995. Now that genomic data are being utilized in many countries, this has led to two serious problems. The first is that of preselection of young bulls such that the young animals are no longer a random sample of progeny from a sire by dam mating pair. Secondly, some countries are becoming less willing to share genomic data with Interbull. Both issues raise concern over the future of Interbull and international comparisons. This paper suggests a competition model as a potential replacement for MACE. The competition model makes pairwise comparisons between all pairs of bulls within a country and combines these differences across countries through bulls that are used in more than one country. Pedigree information is ignored as are all genomic data because bulls are treated as fixed. The model produces one international ranking of bulls averaging out any genotype by environment interactions which may exist. The competition model is illustrated by a small example. The limitations and advantages of the competition model are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".