Copolymerization of ethylene and ?-olefins with combined metallocene catalysts. III. Production of polyolefins with controlled microstructures
Bibliographic record
Abstract
The distributions of the molecular weight (MWD) and chemical composition of polyolefins can be controlled by the combination of two or more metallocenes from the knowledge of the behavior of each individual metallocene. Polyolefins with bimodal MWDs and a higher comonomer content in high molecular weight chains have physical properties suitable for advanced applications such as pipes for gas and water distributions. With conventional Ziegler–Natta catalysts, this type of polymer resin is produced only by tandem reactor technology in which two or more polymerization reactors are used in series. With combined metallocene catalysts, similar polymer resins can be produced in a single reactor. The versatility of these combined metallocene catalysts is investigated in this article. © 2000 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 38: 1427–1432, 2000
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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 | 0.000 | 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) | 0.002 | 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".