Selecting optimal follow-up split-plot designs
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
Split-plot designs and follow-up designs have been considered in literature for many years. However, no research has been done on selecting optimal follow-up split-plot designs for response surface methodology. In this thesis, we will find optimal follow-up split-plot designs. We modify Pareto-based coordinate exchange algorithm and apply it to follow-up split-plot designs. The algorithm allows for selecting optimal follow-up designs based on multiple criteria under user-specified models. We use DP- and I-criteria to select optimal follow-up split-plot designs. D$_{s}$ criterion and potential bias caused by model misspecification are also considered to further select optimal designs. Examples are given to illustrate the methods of selecting follow-up designs. Different follow-up run sizes are considered. Comparisons are made among the combined designs with the same follow-up run size.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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