343. Subsetted orthogonal data augmentation for fast parallel implementation of Bayesian models for whole-genome analyses
Bibliographic record
Abstract
A novel subsetted orthogonal data augmentation (SODA) approach was proposed for fast parallel implementation of Bayesian linear regression models for whole-genome analyses. Performance was evaluated using a simulated data set from the QTL-MAS XIV workshop. Computing speed, convergence, and genomic prediction accuracy and bias were compared to those using a regular Gibbs sampling algorithm and the original orthogonal data augmentation (ODA). Results showed that the SODA approach required less time and number of operations to compute per CPU than the ODA and regular algorithms. The SODA algorithm converged faster and had larger effective sample size than the ODA algorithm. Genomic prediction accuracy and bias were similar among different approaches. The proposed SODA approach will be especially useful when the number of markers is very high, where a regular algorithm or the ODA approach are infeasible or takes too long to complete.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".