Bibliographic record
Abstract
Statistical indicators to quantify information in national GEBV are required for optimal weighting of these GEBV as input observations for GMACE. The weighting factors are also used when approximating reliabilities of GMACE output, the international GEBV. The present study considered a change in approach from providing a recipe to national centers for deriving the weighting factors, to instead having Interbull apply the recipe internally and in a guaranteed consistent way for all countries. The main impacts of this change in approach were on selected estimates of genomic variance and GMACE reliabilities, most notably for specific estimates that had previously been questioned as erroneous. Most estimates of variance and reliability were essentially unaffected by the change in approach, because most weighting factors were only slightly different than before. An advantage of the new approach was that weighting factors and GMACE model specifics became perfectly aligned, and thus GMACE reliabilities were aligned without exception to the national values. All GMACE reliabilities were equal or higher than national values. All increases in reliability with GMACE were as expected, only for bulls that had input GEBV from more than one country, and with smaller increases if the multiple GEBV were from countries that share data for national genomic predictions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.057 | 0.008 |
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".