Process Capability Indices for Processes when the Underlying Data are Interval-Valued
Why this work is in the frame
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Bibliographic record
Abstract
One of the important activities of process quality management is to see that the processes of interest are, in fact, stable and capable. In this paper, the problem of obtaining process capability indices (PCIs) for the processes when the underlying data are interval-valued is considered. Since interval-valued data such as systolic and diastolic readings have specifications for both lower and upper values, drawing PCIs cannot be straightforward. In this paper, we attempted to build connections between the lower and upper specifications limits based on which the resulting PCIs are drawn. This is done by considering the coefficients of inflation and the mean shift values of distributions of both lower and upper values of the interval-valued data. The new expressions for the proposed PCIs are determined. We have considered the systolic and diastolic data to demonstrate the computations of PCIs.
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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 | 0.030 | 0.383 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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