Bibliographic record
Abstract
Estimation of genomic variances for GMACE is similar to the estimation of genetic variances for MACE, and is based on REML equations that include a prediction error variance term. For GMACE, the prediction error variances must be approximated, and this approximation was improved in the present study to better reflect statistical covariance between the national GEBV of young bull and MACE EBV of parents. In previous estimation, some parental information was being excluded, allowing a simple approximation to work well, but the simple approximation did not work well after including all parental information. Use of any approximation can bias genomic variance estimates, and such bias would adversely affect conversions of genomic information from the evaluation scales of GEBV to non-GEBV countries. This bias was minimized by scaling, to eliminate across-country average difference between estimated variance from GMACE relative to MACE. This MACE-neutral scaling of genomic variance estimates does not affect bull comparisons among GEBV countries, because it does not alter relative genomic variances. However, it should improve comparisons between GEBV and non-GEBV countries. The new estimates of genomic variances were very similar to previous estimates from the same data. Some individual estimates changed, but the rankings of countries from high to low variance were nearly identical as before. As such, all GMACE bull rankings were nearly identical to previous rankings, with correlations higher than 0.997 for all 5 traits studied and all country scales. Standard deviations of GMACE predicted breeding values were also very similar, within 1% of the previous in almost all cases.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".