Finding Robust Response Surface Designs With Blocking Using a Model-Weighted A-Optimality Criterion
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new approach to finding robust response surface designs that can accommodate potential model misspecifications. To achieve this, experimental designs that are robust across all potential models were considered prior to data collection. Blocking effects were combined into all possible models, and the set of all reduced models was obtained using the weak heredity principle. The objective of this study was to propose the use of the geometric mean of A-optimalities as a new weighted A-optimality criterion for finding robust response surface designs. Both a genetic algorithm (GA) and an exchange algorithm (EA) were employed to optimize the weighted A-optimality criterion and compared with the widely used central composite design. The weighted A-optimal designs generated by GA and EA in this study had higher Aw and A-efficiencies than CCD, and the Aw-optimal designs generated by the GA were as or more efficient than the EA.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it