A Quality Framework to check the applicability of engineering and statistical assumptions for automated gauges
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
In high-volume part manufacturing, interactions between program data and program flow can depart significantly from the initial statistical assumptions used during software development. This is a particular challenge for industrial gauging systems used in automotive part production where the applicability of statistical models affects system correctness. This paper uses a Quality Framework to track high-level engineering and statistical assumptions during development. Statistical Process Control (SPC) metrics define an “in-control” region where the statistical assumptions apply, and an outlier region where they do not apply. The gauge is monitored on-line to verify that production corresponds to the area of the operation where the gauge algorithms are known to work. If outliers are detected in the on-line manufacturing process, then parts can be quarantined, improved gauging algorithms selected, and/or process improvement activities can be initiated.
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 | 0.003 |
| 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) | 0.000 | 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