Copolymer Composition Deviations from Mayo–Lewis Conventional Free Radical Behavior in Nitroxide Mediated Copolymerization
Bibliographic record
Abstract
At the base of page 261 in this manuscript, an incorrect figure citation was given. The text states “It is interesting to note that δIT,S starts with a negative value in the early stages of the reaction (Figure 9a)…”. However, Figure 9a is not related to this text. The correct, related figure is now provided here: Table 5 in this manuscript contained several errors. The full and correct Table 5 is now presented here: The last paragraph on page 262 also contained errors to the numbers given. The full, corrected paragraph is now presented here: “The model indicates that the equilibrium of the methacrylic-end chains is reached faster than the equilibrium of the aromatic-end chains, and therefore δAD,M approaches zero at lower conversion (around 0.07%) than δAD,S (around 5%). However, the magnitudes of the d-ps are very small and the imbalance between both activation–deactivation processes generates only a minor difference in composition with respect to the ML prediction; the instantaneous composition exhibits a maximum deviation of 0.5 pp at 4% of conversion, and at 5.5% of conversion the ML and M2 composition curves are practically equal since the QEC has been reached. The corresponding deviation between the cumulative composition curves, after a peak of nearly 0.5% at 5.5% conversion, stays below 0.25% during most of the reaction and becomes essentially zero after 60% conversion.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".