An EWMA -based method for monitoring polytomous logistic profiles
Why this work is in the frame
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Bibliographic record
Abstract
In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where the quality characteristic can be modeled using dichotomous or polytomous variables. Polytomous variables, especially ordinal variables, have various applications. An ordinal (or ordered) variable is a categorical variable whose values are related in a greater/lesser sense. In this paper, we proposed three methods for monitoring a profile when the process/service output is an ordinal response variable. Ordinal logistic regression (OLR) provides the basis for our profile model. Three methods including chi-square statistics, exponentially weighted moving average (EWMA) statistics, and combination of these two statisticsare proposed to monitor OLR profiles in phase II. The performances of these three methodsare evaluated by average run length criterion (ARL).
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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