Bayesian Simple Step–stress Acceleration Life Testing Plan under Progressive Type-I Right Censoring for Exponential Life Distribution
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Bibliographic record
Abstract
This paper discusses the design of the optimal SSALT plan using Bayesian approach and progressive Type-I right censoring for an exponential life distribution under large sample size and small censoring proportion. The cumulative exposure model and the exponential life distribution in both steps are assumed. The progressive Type-I right censoring can reduce the cost of the test. This reduction, unfortunately, comes on the expense of reducing the precision of the test. The optimal test parameters, the stress changing time and the first step stress, are obtained by minimizing the expected variance of the life for the pth percentile using Bayesian approach. A comparison between conventional Type-I and progressive Type-I right censoring is also provided. The results showed that progressive Type-I right censoring is recommended when strong prior information for the model parameters is used as the test precision becomes less sensitive to the censoring proportion.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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