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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".