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Record W2153918878 · doi:10.1002/mats.201200058

Mathematical Modeling of Arborescent Polyisobutylene Production in Batch Reactors

2013· article· en· W2153918878 on OpenAlex
Yutian R. Zhao, Kimberley B. McAuley, Judit E. Puskás, Lucas M. Dos Santos, Alejandra Alvarez

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecular Theory and Simulations · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthUniversity of Akron
KeywordsProduction (economics)Process engineeringPulp and paper industryBiochemical engineeringBiological systemChemistryMaterials scienceEnvironmental scienceMathematicsEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract A novel model describes copolymerization of isobutylene and inimer (initiator‐monomer) via living carbocationic polymerization. Six different propagation rate constants and two types of equilibrium reactions are considered. Simplifying assumptions are made to enable implementation in PREDICI, so that the molecular weight distribution (MWD) could be predicted for molecules with different branching levels. Four apparent rate constants were estimated from experimental data with <5 branches per molecule. Model predictions provide a good fit to data, and simulation results show that polymers with high‐branching levels and ≥15 inimer units contribute significantly to the MWD, even though their concentrations are very low. magnified image

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 score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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