Determination of sample size to support diagnostic inspection of components
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 complex engineering system like a nuclear power reactor consists of a large variety and number of engineering components. As a part of a component aging management program, the diagnostic inspections of various component populations are performed to detect the onset of any unanticipated degradation. A prudent selection of the inspection sample size is necessary to optimise inspection cost. Sample size selection is typically based on the traditional statistical hypothesis test, which tends to result in a fairly large sample size. This paper presents an alternate approach to the sample size determination (SSD) problem based on the concept of the value of information (VoI). The paper provides a comparative analysis of the VoI and hypothesis-testing approaches through illustrative examples. The VoI approach is shown to provide a more meaningful way to minimise the cost of inspection as a function of component-replacement cost and losses arising from a failure. The characteristics and advantages of the VoI approach are analysed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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