325. An iterative method for deregressed proof in single-step genomic BLUP and its application to a dairy cattle population
Bibliographic record
Abstract
The objective of this study was to describe an iterative algorithm to deregress genomically-enhanced breeding values (GEBV) in single-step genomic BLUP (ssGBLUP). The method is an extension of the previous work by the first two authors, who gave a deregression method in singe-step SNP BLUP. The inverse of the unified relationship matrix (H-1), which is a function of the inverse of genomic relationship matrix (G-1) and the inverse of the additive relationship matrix (A-1), was considered in the deregression method. Effective daughter contribution (ERC) for bulls or effective record contribution (ERC), which are functions of reliability of GEBV, should be used as weights on deregressed proof. The deregression algorithm consists of a series of matrix-vector multiplications, and the number of iterations is expected to be limited. The computational cost of this approach was discussed. Further research is needed to confirm the ‘reversibility’ of deregressed GEBV.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".