Reactivity Ratio Estimation in Radical Copolymerization: From Preliminary Estimates to Optimal Design of Experiments
Bibliographic record
Abstract
An error-in-variables-model (EVM) framework is presented for the optimal estimation of reactivity ratios in copolymerization systems. This framework consists of several sequential steps and practical prescriptions that can yield reliable and statistically correct reactivity ratio values. These steps include: (a) screening experiments for estimating preliminary reactivity ratios, (b) optimal design of experiments, (c) full conversion range experiments and estimation of optimal reactivity ratios, and if necessary, (d) design of sequentially optimal experiments and re-estimation of reactivity ratios and diagnostic checks. This complete methodology should become common practice for determining reactivity ratios with the highest possible level of confidence. The performance of this framework is verified experimentally with data from the controlled nitroxide-mediated copolymerization of 9-(4-vinylbenzyl)-9 H -carbazole (VBK) and methyl methacrylate (MMA), a novel and largely unstudied copolymer system.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".