Accounting for Test Variability Through Sizing Local Domains in Sequential Design Optimization With Concurrent Calibration-Based Model Validation
Bibliographic record
Abstract
We have recently proposed a new method for combined design optimization and calibration-based validation using a sequential approach with variable-size local domains of the design space and statistical bootstrap techniques. Our work was motivated by the fact that model validation in the entire design space may be neither affordable nor necessary. The method proceeds iteratively by obtaining test data at a design point, constructing around it a local domain in which the model is considered valid, and optimizing the design within this local domain. Due to test variability, it is important to know how many tests are needed to size each local domain of the sequential optimization process. Conducting an unnecessarily large number of tests may be inefficient, while a small number of tests may be insufficient to achieve the desired validity level. In this paper, we introduce a technique to determine the number of tests required to account for their variability by sizing the local domains accordingly. The goal is to achieve a desired level of model validation in each domain using the correlation between model data at the center and any other point in the local domain. The proposed technique is illustrated by means of a piston design example.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".