Demystifying the estimation of reactivity ratios for terpolymerization systems
Bibliographic record
Abstract
In typical practice for terpolymerizations so far, binary reactivity ratios have been used directly in the instantaneous Alfrey and Goldfinger (AG) model, effectively ignoring the presence of the third monomer. In addition, the use of the AG model leads to severe estimation problems, if one would like to estimate reactivity ratios from experimental data. Due to the above reasons, the AG model was recast and was subsequently used with terpolymerization data directly to estimate ternary reactivity ratios under the error‐in‐variables‐model framework, based both on instantaneous (low conversion) and cumulative composition data (medium and high conversions). Several examples and counter examples highlight such important issues as the choice of the correct number of responses, accounting for the appropriate error structure, and incorporating the right information content, all with diagnostic checks whose target is the eventual reliability of the reactivity ratio estimates. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1752–1766, 2014
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".