Minimum Aberration Factorial Designs Under A Mixed Parametrization
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Bibliographic record
Abstract
The baseline parametrization for two-level factorial designs has been receiving increasing attention recently.While the orthogonal parametrization is appropriate for experiments where the two levels of each factor are symmetrical, the baseline parametrization is well suited for experiments where the two levels of each factor are asymmetrical and one level, called a baseline level, is the default level.This paper considers a general situation where some factors have a baseline level while others do not.A mixed parametrization of factorial effects is proposed and its connection with the existing parametrizations is established.Under this new parametrization, we show that orthogonal arrays continue to be optimal for estimating main effects, and then put forward two minimum aberration criteria for further design selection.Both theoretical and algorithmic constructions of minimum aberration designs are examined and useful designs are obtained.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it