Unit exponentiated Weibull model with applications
Why this work is in the frame
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Bibliographic record
Abstract
Developing new effective statistical distributions tailored to model data on the unit interval is essential in modern data analysis. In this article, we propose a novel distribution on the unit interval, termed the unit exponentiated Weibull distribution, derived from the three-parameter exponentiated Weibull distribution. We will explore the key statistical properties of this new distribution and employ the maximum likelihood and least squares methods for parameter estimation. A simulation study is conducted to evaluate the performance of the maximum likelihood and least squares estimates. The simulation study demonstrates that the maximum likelihood method outperforms the least squares method in estimating the three parameters of the underlying model. To illustrate the practical utility of the proposed model, we analyze real-world datasets and compare its performance against other established distributions. The results show that the proposed model provides a superior fit to the competing models considered in the study.
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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 | 0.000 |
| 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