Techniques for kinetic parameter estimation in free radical polymerization models
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Bibliographic record
Abstract
Free radical polymerization (FRP) systems can have many reactions, leading to many kinetic parameters. The most common method to obtain values for kinetic parameters is weighted-least squares estimation, which uses multiple types of measured responses. Error-in-variables model estimation is used when there is significant uncertainty in the model inputs. When FRP models have many unknown parameters, it is difficult to estimate them all uniquely, so modelers often resort to model simplification or subset selection methods for parameter estimation. The aim of this review is to describe the most common techniques that modelers use for kinetic parameter estimation in FRP models. • Free radical polymerization models have a large number of kinetic parameters. • Weighted least-squares is the most common type of parameter estimation. • Error-in-variables model estimation is used to account for uncertain inputs. • Subset selection is used to estimate parameters while avoiding overfitting of data. • Direct experimental methods can estimate propagation and termination constants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it