A New Flexible Extension of the XLindley Distribution with Properties and Application on Income Tax and Carbon Fibers Data
Why this work is in the frame
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Bibliographic record
Abstract
A new and enhanced extension of the XLindley distribution termed the sized-biased XLindley distribution (SBXLD), has been introduced. This new model explores two specific variants: the length-biased XLindley distribution and the area-biased XLindley distribution. Various crucial properties such as moments, moment generating function, quantile function, survival, and hazard functions, mean residual life function, and Rényi entropy have been derived and extensively investigated. For parameter estimation, five distinct methods have been employed to estimate the model parameters. Through a comprehensive simulation study, the most effective estimation method has been identified. The applicability and efficiency of the SBXLD model have been demonstrated using two datasets from different domains. It has been observed that the SBXLD model effectively analyzed these datasets and yielded superior results compared to other competitive distributions under consideration.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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