Accuracy of MACE evaluation for composite type traits compared to prediction based on linear traits
Bibliographic record
Abstract
For several years, Interbull evaluations for conformation traits have been available for Brown Swiss, Guernsey, Holstein and Jersey breeds. In November 2003, Interbull started computing international evaluations for Ayrshire Conformation Traits. CDN publishes MACE EBV for all linear traits, and predicted EBV for all major trait composites, with the exception of overall conformation. Composite traits are predicted using many linear traits with a multiple regression. Results for the Ayrshire breed have highlighted some problems with conformation: low genetic correlations across countries and some countries that did not provide overall Conformation to Interbull. Problems with low correlations were already present in other breeds for the same trait. Average genetic correlations between Canada and other countries for conformation are .493, .666, .715, .761 and .604 in Ayrshire, Brown Swiss, Guernsey, Holstein and Jersey, respectively (Table 1).
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".