Analysis of Ethylene/1-Olefin Copolymers Made with Ziegler-Natta Catalysts by Deconvolution of Molecular Weight and Average Short Chain Branching Distributions
Bibliographic record
Abstract
In this article a systematic method is proposed to deconvolute the time-dependent molecular weight distributions (MWD) and average comonomer fraction profiles of ethylene/1-olefin copolymers made with heterogeneous Ziegler–Natta catalysts. These distributions with a high-temperature gel permeation chromatography equipped with an infrared detector at four different polymerization times have been measured and used this information to infer how the fractions of polymer made on each site type varied with polymerization time. The model estimates here the minimum number of active site types needed to describe these copolymers, the MWD of polymer populations made on each site type, and their average comonomer fractions. This method is useful to quantify the microstructure of olefin copolymers made with multiple site type catalysts using the least number of adjustable parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".