Orthogonal-array composite minimaxloss designs for third-order models
Why this work is in the frame
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Bibliographic record
Abstract
Missing observations that occur during experiments constitute a major source of error in experimental results. Therefore, robust designs are chosen to avoid or reduce the impact of these missing observations. In this work, the effects of one missing observation in various design portions on the precision of parameter estimates and maximum prediction variance of third-order orthogonal-array composite designs (OACDs) are examined. This is done for 4 ≤ k ≤ 8 factors using 1 ≤ nc ≤ 5 center points at different distances of a non-zero coordinate (i.e., values of α). Based on the structure of third-order OACDs, the orthogonal-array composite minimaxloss designs (OACMDs) in the presence of missing observations are constructed. The newly constructed OACMDs are compared with the existing OACDs based on relative A-, D-, and G-efficiency values and generalized scaled-standard deviation (GSD). The results show that missing a factorial and axial point has a significant effect on OACDs, while missing a center point has little or no effect on the precision of the parameter estimates and maximum prediction variance. OACMDs generally perform better than OACDs in terms of A-, D-, and G-efficiency values and GSD.
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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 | 0.033 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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