Entropy-Guided Reverse Deconvolution of Polyolefin MWDs into Flory Most Probable Distributions
Bibliographic record
Abstract
Multiple-site-type (MST) catalysts, such as heterogeneous Ziegler–Natta, Phillips, and some supported metallocenes, make polyolefins with broad molecular weight distributions (MWDs) that cannot be described with a single fundamental equation. Since polyolefin properties depend on their MWDs, accurate mathematical models for these distributions are essential in polymer reaction engineering. A popular modeling approach is to deconvolute their MWDs into several Flory most probable distributions (MPD). All previous deconvolution algorithms start by fitting an experimental MWD with a few Flory MPDs and then increase their number until the deviation between measured and predicted MWDs stops decreasing (forward deconvolution). This article reverses this approach, initially fitting experimental MWDs with multiple Flory MPDs and then decreasing their number with the assistance of a penalty function based on Shannon entropy (reverse deconvolution). The reverse method is easier to implement than the traditional forward methods and simplifies the automation of MWD deconvolution methods.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".