Distance correlation-based nearly orthogonal space-filling experimental designs
Why this work is in the frame
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Bibliographic record
Abstract
In many real-world applications of optimisation, the required model (function) evaluations are determined by expensive and time-consuming physical experiments or numerical procedures. Within this context, the objective of experimental design is to obtain meaningful information based on a strongly limited number of experiments or function evaluations. In order to generate informative designs, space-filling and orthogonality are widely considered to be essential. In this study, we review several existing design performance metrics that address these criteria, and the distance correlation-based metric is proposed for achieving improved experimental design. Three closely related randomised sampling schemes are proposed to generate nearly orthogonal designs with good space-filling properties in real time. The effectiveness of our approach is demonstrated by numerical examples and an illustrative borehole model case study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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