Allocation of simulation effort for neural network vs. regression metamodels
Why this work is in the frame
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Bibliographic record
Abstract
The construction of a neural network simulation metamodel requires the generation of training data; design points (inputs) and the estimate of the corresponding output generated by the simulation model. A common methodology is to focus some simulation effort in obtaining accurate estimates of the expected output values by executing several simulation replications at each point and taking the average as the estimate. However, with a limited amount of simulation effort available and a rather large input space, this approach may not produce the best expected value approximations. An alternate approach is to distribute that same simulation effort over a larger sample of input points, even if it means the resulting estimates of the expected outputs at each point will be less accurate. We will show through several examples that this approach may result in better neural network metamodels; this conclusion differs from other studies involving regression metamodels.
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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 | 0.001 |
| 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