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Record W2278569634 · doi:10.1002/masy.201500111

Comparison of Different Dynamic Monte Carlo Methods for the Simulation of Olefin Polymerization

2016· article· en· W2278569634 on OpenAlex
Amanda L. T. Brandão, João B. P. Soares, José Carlos Pinto, André L. Alberton

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 Symposia · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMonte Carlo methodComputationPolymerizationComputer scienceOlefin fiberWork (physics)Olefin polymerizationPolymerAlgorithmMaterials scienceMathematicsThermodynamicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Summary In this work, Monte Carlo methods were used to simulate olefin polymerization with coordination catalysts: the Direct method (DM), the First Reaction method (FRM), the Next Reaction method (NRM), and the τ‐Leaping method. The first three methods are exact stochastic simulation algorithms (SSA), while the τ‐Leaping is an approximate method with faster computation times. It is shown that all four methods predict similar polymer microstructures, but require significantly different computation times. The τ‐Leaping method is the fastest, being recommended when complex polymerization mechanisms are being investigated. The NRM, because of its intelligent data storage and handling approach, is the best among the SSA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.258

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)0.0000.000

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 head0.024
GPT teacher head0.360
Teacher spread0.336 · 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