Predicting MACE breeding values on a phantom country scale
Bibliographic record
Abstract
The estimation of international breeding values provided by INTERBULL results in MACE proofs on single trait base. These proofs can be used within each member country, but the comparison between them is not possible. A combination of several MACE proofs to a combined value helps breeders in non-Interbull member countries to select bulls from these countries disregarding national base differences. We provide the first application of a combined MACE proof based on phantom country scale, comparing two different approaches. The country list is the PROTEJE group, which supported this project. The estimated combined proofs show an adequate trend according to single MACE proofs. Overand underestimations, as for average proofs, cannot be seen. Double information does not bias the results. Top lists for combined proofs contain top sires of all participating countries. Introduction International breeding value estimation provided by INTERBULL is very useful to compare bulls with different countries of origin. The used MACE-method provides proofs for each sire on each member country scale for marketing strategies within each member country. With actual 25 participating countries, the same number of different proofs is estimated for each bull. This large number makes it difficult for breeders in non-member countries, e.g. China, to compare international sires on different scales. In the future it will not become possible for a single European country to compete against dominating global players. Only collaborations of European associations will be large and powerful enough to exist on a long term perspective. In order to put European associations on a common platform, the calculation of joint European sire proofs will be necessary to present one joint breeding value on outside European markets. This helps to support marketing strategies and enables to compete against other large breeding areas, i.e. United States of America or Canada. The development of new worldwide markets needs a powerful and compact appearance of all European countries to outside European markets, disregarding inside competition. It has been discussed in the literature for years if a combination of INTERBULL proofs will be helpful to calculate country-independent sire proofs. Several approaches were discussed, the most interesting and favoured approach is the ‘MACE proof on a phantom country scale’ (Liu, 2007; Mark, 2006; Torsell, 2007). To combine European MACE proofs to one single European breeding value, the PROTEJE-workgroup has been founded. Goal of this project is a single European sire proof list to combine proofs on one common European scale. The method used in this project is similar to the above but used on a smaller number of countries.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".