Comparison of Probability Distribution Functions for Fitting Distillation Curves of Petroleum
Why this work is in the frame
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Bibliographic record
Abstract
The fitting capability of 25 probability distribution functions for distillation data of petroleum fractions was analyzed in this work. Rankings of all the functions based on two different approaches were established after a statistical analysis of the fit of the functions with a data set of 137 distillation curves. In general, distribution functions with four parameters showed better fitting capability than those with three parameters. Two-parameter functions were not effective in fitting distillation data. The Weibull extreme, Kumaraswamy, and Weibull functions were found to be the best distribution functions for fitting distillation data considering their ranking and the required CPU time. These distribution functions exhibited the lowest Akaike information criterion and Bayesian information criterion average values, standard deviations lower than 1%, correlation coefficients higher than 0.999, and residuals randomly distributed without any tendency. The fitting capability of the best functions was validated with an independent set of distillation data, and the ranking was the same.
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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