Reactivity Ratio Estimation from Cumulative Copolymer Composition Data
Bibliographic record
Abstract
Abstract The goal is to present an alternative technique for reactivity ratio estimation in copolymerization. Typically, reactivity ratios are estimated using the instantaneous copolymer composition equation, based on low conversion copolymer composition data. However, using experimental data from the full copolymerization trajectory would, in principle, be more advantageous, and shy away from commonly used restrictive assumptions. Estimation using cumulative copolymerization data and models eliminates the difficulties associated with stopping reactions at low conversion, while one gains to study the full polymerization trajectory. The error‐in‐variables‐model (EVM) method is used for parameter estimation. Two cumulative model forms, the analytical integration of the differential composition equation and the one resulting from the direct numerical integration of this equation, are employed. Using these two types of models improves the reactivity ratio estimation and, in particular, the latter model form is a more reliable and direct method of estimating reactivity ratios. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".