Blocked Fractional Factorial Split-Plot Experiments for Robust Parameter Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fractional factorial experiments are commonly used for robust parameter design and, for ease of use, such experiments are often run as split-plot designs. If the control factors are at the subplot level and the noise factors are at the whole-plot level, this also results in gains in efficiency. If all runs of the fractional factorial split-plot design cannot be run under homogeneous conditions, such designs are frequently blocked. In this paper, we explore the choice of blocked fractional factorial split-plot designs for use in robust parameter design. A ranking scheme for such designs is developed and, using a search algorithm, a catalog of 32-run optimal designs is provided. Two situations are considered, one in which the control factors are at the subplot level and one in which the control factors are at the whole-plot level. An example from the aerospace sector is used to illustrate the concepts.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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