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Record W2280359110 · doi:10.1002/mats.201500059

Advanced Monte Carlo Modeling Using Weight‐Based Selection of Arborescent Polyisobutylene Molecules in a Batch Reactor

2016· article· en· W2280359110 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecular Theory and Simulations · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonte Carlo methodBranching (polymer chemistry)ComputationPolymerMolar mass distributionSelection (genetic algorithm)MoleculeStatistical physicsMaterials scienceBiological systemChemistryAlgorithmComputational chemistryMathematicsComputer sciencePhysicsOrganic chemistryComposite materialStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

An advanced Monte Carlo (MC) method is developed, using weight‐based selection of polymer chains, to predict the molecular weight distribution (MWD) and branching level for arborescent polyisobutylene ( arb PIB) at the end of a batch reaction. This new weight‐based MC method uses differential equations and random numbers to determine the detailed structure of arb PIB molecules. Results agree with those from an advanced number‐based MC method. The proposed weight‐based algorithm requires approximately twice the computation time of the number‐based method, but produces more accurate results in the high‐molecular‐weight portion of the MWD when the same number of polymer chains is assembled. 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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