On the construction of saturated split-plot designs for quadratic response surface models
Why this work is in the frame
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Bibliographic record
Abstract
Split-plot designs are commonly employed in experiments where the levels of some factors are difficult or costly to adjust. One of the primary objectives of many such experiments is to accurately predict responses using fitted response surfaces. While many split-plot designs have been proposed in the literature for this purpose, most of them require large run sizes and an excessive number of whole plots, resulting in expensive and time-consuming experiments. To address this issue, we conduct a study to determine the minimum requirements for a split-plot design that can be used to fit a response surface. Based on these requirements, we construct saturated or nearly saturated response surface split-plot (RSSP) designs that are cost-effective, featuring the fewest possible run sizes and the smallest number of whole plots. We use a motivating example and conduct simulations. Our study shows that the proposed RSSP designs not only provide cost savings, but also maintain good predictive efficiency.
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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 | 0.011 | 0.006 |
| 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 | 0.000 |
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