Experimental methods and data evaluation procedures for the determination of radical copolymerization reactivity ratios from composition data (IUPAC Recommendations 2025)
Bibliographic record
Abstract
Abstract This recommendation defines the preferred methodology for determining reactivity ratios from copolymer composition data using the terminal model for radical copolymerization. The method is based on measuring conversion ( X ) and copolymer composition ( F ) of three or more copolymerization reactions conducted with different initial monomer compositions ( f 0 ). Both low and high conversion experiments can be combined, or alternatively only low conversion experiments can be used. The method provides parameter estimates, but can also reveal deviations from the terminal model and the presence of systematic errors in the measurements. Special attention is given to error estimation in F and construction of the joint confidence interval for the reactivity ratios. Previous experiments measuring f 0 − F (i.e., copolymer composition as a function of varying f 0 ) or f − X (i.e., how f varies with X in an experiment) can also be analyzed with this IUPAC recommended method. The influence of systematic errors in the measurements on the reactivity ratio determinations is addressed. The document has a broad significance in that it seeks to eradicate the use of incorrect methods and common mistakes in determining reactivity ratios in radical copolymerizations.
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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 | 0.045 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.065 |
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".