The Exponentiated Kumaraswamy-Weibull Distribution with Application to Real Data
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Bibliographic record
Abstract
A new five-parameter lifetime distribution called the exponentiated Kumaraswamy-Weibull distribution is introduced. It includes several important sub-models as special cases such as exponentiated Weibull, Kumaraswamy-Weibull, exponentiated exponential, exponentiated Rayleigh and Weibull. Essential mathematical and statistical properties for the distribution are presented. A proximate form of the mode is derived and it can be used to derive mode forms of other well-known distributions. Important parametric characterizations for probability density and hazard functions are discussed. The estimation of the parameters by maximum likelihood method is discussed. Three real data sets are used to show its excellent performance fit over existing popular lifetime models. It is effective model to analyze several positive data sets.
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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 | 0.000 |
| 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