Statistical methods for stress screen development
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
Stress screening during design, development, and production of electronic hardware is a quality improvement technique which can be employed to reduce defects in a product. However, due to the variety of electronic hardware types which may be screened and the number of stresses which may be applied for screening, there are no commercial standards which describe how to develop an effective stress screen. This paper describes a non-product-specific screen development technique which utilizes statistical analysis methods to achieve an effective and efficient stress screen. Statistical applications for various aspects of stress screen development are suggested, including Pareto analysis, Exploratory Data Analysis (EDA), Weibull analysis of time-to-failure data, comparison of means, analysis of variance (ANOVA), use of statistical process control charts (CUSUM, X-bar R), Duane plots of reliability growth, and use of the Poisson distribution for determining sample screen sizes. The techniques outlined involve test and analytical activities applied throughout product development; from first prototypes through to volume production. The use of statistical methods allows for development of an effective screen to remove defects and for an effective risk assessment of the effect of defects through numerical quantification of defect probabilities.
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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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