Optimal progressively censored reliability sampling plans for log-location-scale distributions
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Bibliographic record
Abstract
Here, we introduce a variable neighborhood search algorithm-based approach to determine the minimum sample sizes required for progressively censored reliability sampling plans within the flexible family of log-location-scale family of distributions, which includes Weibull and log-logistic distributions. The proposed method significantly reduces sample sizes compared to previously developed approaches, demonstrating its feasibility, especially for small sample sizes, in contrast to complete search methods. Optimal censoring plans are identified using A- and D-optimality criteria, a variance-measure criterion, and a cost function-based criterion. The proposed approach consistently outperforms the methods discussed in the literature for the case of Weibull distribution. Furthermore, an additional optimization problem with a cost constraint is addressed to illustrate the practicality and efficiency of the proposed method, and a sensitivity analysis is also carried out.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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