Analysis Methods for Computer Experiments: How to Assess and What Counts?
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
Statistical methods based on a regression model plus a zero-mean Gaussian process (GP) have been widely used for predicting the output of a deterministic computer code. There are many suggestions in the literature for how to choose the regression component and how to model the correlation structure of the GP. This article argues that comprehensive, evidence-based assessment strategies are needed when comparing such modeling options. Otherwise, one is easily misled. Applying the strategies to several computer codes shows that a regression model more complex than a constant mean either has little impact on prediction accuracy or is an impediment. The choice of correlation function has modest effect, but there is little to separate two common choices, the power exponential and the Matérn, if the latter is optimized with respect to its smoothness. The applications presented here also provide no evidence that a composite of GPs provides practical improvement in prediction accuracy. A limited comparison of Bayesian and empirical Bayes methods is similarly inconclusive. In contrast, we find that the effect of experimental design is surprisingly large, even for designs of the same type with the same theoretical properties.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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