Identification of Concurrent Control Chart Patterns in Time Series
Why this work is in the frame
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Bibliographic record
Abstract
Control chart patterns (CCPs) can be associated with certain assignable causes, and recognition of such patterns can assist the diagnostic search for those causes. Variations could be one or more instances of trend, cyclic, hugging, sudden shift or some other variations over time. Each pattern has special statistical characteristics which differentiate one pattern from another. In a time series, presence of more than one pattern may exist and identification of concurrent pattern is important. In this paper, we will utilize a new approach, RobustICA, for identification of concurrent patterns which is efficient when compared to traditional approaches being used for feature extraction. It will identify independent components hidden in mixture patterns and input those independent components to decision trees for recognition of as many as eight separate control chart patterns.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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