Identifying Process Deterioration in Weighted Exponentially Distributed Time Between Events
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 observational studies, the probability of selection of sampling units is not always equal. The recorded observations are biased in this scenario. The unweighted distributions in such situations are not useful until the inclusion probability of each item is same. The theory of weighted distributions offers a unifying approach for these types of conditions because it considers the adjustment bias. Failure to comply with such adjustment may lead to inappropriate results. In this article, an efficient mentoring scheme (Weighted-TBE chart) for time between events (TBE) using weighted exponential distribution has been proposed based on weighted variance (WV) method. A comparison has been established between CC based on weighted and unweighted probability distributions. The performance measure ARL has been calculated using Monte Carlo simulations. The Weighted-TBE chart has provided least values of ARL in the presence of unwanted process variations and proved to be more effective than the existing scheme. Further the proposed control chart has been applied to time between failures data to show its practical applicability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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