GMACE pilot #4: Adjusting the national reliability input data
Bibliographic record
Abstract
International standards do not exist for the approximation of national genomic reliabilities, which are used as input data for the GMACE international genomic evaluation system. The focuses of the present study were to develop a method of reducing differences among the national reliabilities approximated by different countries, to apply GMACE using modified national reliabilities, and to use cross-validations tests to determine if GMACE results could be measurably improved. A non-linear international regression model was applied to the average national reliabilities provided by countries for use in GMACE. Residuals of prediction for the average national reliabilities were smaller, indicating greater consistency among the approximations of different countries, for protein and stature relative to traits more difficult to evaluate, such as mastitis, stillbirths and cow conception rate. GMACE input reliabilities were modified by subtracting either some or all of the average prediction error for each combination of trait by country. The impacts of modifying the national reliabilities on GMACE results were relatively small. Predictability of national genomic evaluations by GMACE with only foreign genomic data as input, was essentially the same using either modified or unmodified national reliabilities. However, the international reliabilities produced by GMACE were more consistent if national reliabilities were modified as input and then the modifications were reversed for the GMACE reliability output. The approach was to essentially apply an international standardization of reliability on the way into GMACE and then a de-standardization back to each of the original national scales of expression on the way out.
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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".