Value of Information and Hypothesis Testing Approaches for Sample Size Determination in Engineering Component Inspection: A Comparison
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
A sample size determination method is developed for a two-action problem that represents a component maintenance scenario requiring current state estimation. For safety and generation efficiency, each component of a nuclear power plant must be regularly inspected. In terms of safety, the larger the sample size inspected, the less the uncertainty about current and future states of the components; however, such inspections are expensive. Thus, sample size determination becomes an important problem. A key idea for solving this problem is the Value of Information (VoI) and its derivation: the Expected Net Gain of Sampling (ENGS). The ENGS is a function of sample size and represents by how much a decision maker benefits from the observed data. By maximizing the ENGS, the optimal sample size is determined in terms of cost-benefit analysis.
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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 | 0.054 |
| 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