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 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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".