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Record W2128987968 · doi:10.1002/pola.10701

Modeling the reversible addition–fragmentation transfer polymerization process

2003· article· en· W2128987968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2003
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChain transferChemistryRadical polymerizationReversible addition−fragmentation chain-transfer polymerizationRadicalPolymerizationRaftKinetic chain lengthDisproportionationDispersityPolymer chemistryLiving free-radical polymerizationMolar mass distributionMonomerAdductPolymerFragmentation (computing)KineticsPhotochemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract A kinetic model has been developed for reversible addition–fragmentation transfer (RAFT) polymerization with the method of moments. The model predicts the monomer conversion, number‐average molecular weight, and polydispersity of the molecular weight distribution. It also provides detailed information about the development of various types of chain species during polymerization, including propagating radical chains, adduct radical chains, dormant chains, and three types of dead chains. The effects of the RAFT agent concentration and the rate constants of the initiator decomposition, radical addition, fragmentation, disproportionation, and recombination termination of propagating radicals and cross‐termination between propagating and adduct radicals on the kinetics and polymer chain properties are examined with the model. © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 1553–1566, 2003

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it