[0,1] Truncated Exponentiated Exponential Inverse Weibull Distribution with Applications of Carbon Fiber and COVID-19 Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the recent years the modeling of lifetime data is an essential research topic. Several research studies on this subject have appeared, aiming to introduce new statistical methodologies for dealing with lifetime phenomena. In this paper, we extension Inverse Weibull distribution by using the family [0,1] Truncated Exponentiated Exponential-G family. We get [0,1] Truncated Exponentiated Exponential Inverse Weibull distribution ([0,1]TEEIW). We provide explicit expressions for its properties like: hazard function, quantile function, moments, moment generating function, reliability function, and Order statistics. Three data sets are used to provide the flexibility of new distribution, which represents 63 observations and is about the strength of carbon fibers. And two a COVID-19 mortality rates data set from Italy and Canada. Evidence of this distribution to outperform other classes of lifetime models has been noticed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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