The Integrated Deconvolution Estimation Model: Estimation of Reactivity Ratios per Site Type for Ethylene/1‐Butene Copolymers Made with a Heterogeneous Ziegler‐Natta Catalyst
Bibliographic record
Abstract
Abstract The integrated deconvolution estimation model (IDEM) can be used to estimate the reactivity ratios of multiple‐site‐type catalysts used to make ethylene/α‐olefin copolymers, such as heterogeneous Ziegler‐Natta and Phillips catalysts. The estimation process combines high‐temperature GPC and 13C NMR data to find the reactivity ratios per site type. The IDEM is applied to two sets of ethylene/1‐butene copolymer samples made with an industrial TiCl4/MgCl2 catalyst in the presence and absence of hydrogen. A sensitivity analysis for parameter estimation is developed and the effect of the presence of hydrogen on the reactivity ratio per site type is quantified for the first time for this copolymerization system. magnified image
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".