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Record W2613066032 · doi:10.1002/mats.201700012

Understanding the Microstructure of Living Ethylene/1‐Octene Block Copolymers with Dynamic Monte Carlo Simulation

2017· article· en· W2613066032 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

VenueMacromolecular Theory and Simulations · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Alberta
FundersBeijing Excellent Ph.D. Thesis Guidance FoundationKasetsart UniversityThailand Research FundAssociation for Institutional Research
KeywordsComonomerMicrostructureCopolymerMonte Carlo methodMaterials scienceOcteneEthylenePolymerPolymer chemistryChemistryComposite materialOrganic chemistryMathematics

Abstract

fetched live from OpenAlex

Living ethylene/1‐olefin copolymerization with multiple comonomer feeding stages allows the production of living block copolymers (LBCs) with well‐controlled microstructures. A dynamic Monte Carlo model is developed to simulate the production of LBCs in a semibatch reactor, and it is used to study how the polymer microstructure evolves during the polymerization. The model also describes how chain transfer reactions affect the microstructure of LBC blocks. These model predictions provide useful guidelines for producing LBCs with precisely designed microstructures. 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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