MétaCan
Menu
Back to cohort
Record W2147218730 · doi:10.1002/mren.201100032

Simultaneous Deconvolution of Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Olefin Copolymers: Strategy Validation and Comparison

2011· article· en· W2147218730 on OpenAlexaff
Siripon Anantawaraskul, Warawut Bongsontia, João B. P. Soares

Bibliographic record

VenueMacromolecular Reaction Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCopolymerDeconvolutionMolar mass distributionEthylenePolymerOlefin fiberMaterials sciencePolymer chemistryMicrostructureChemical engineeringCatalysisChemistryOrganic chemistryComposite materialAlgorithmComputer science

Abstract

fetched live from OpenAlex

Abstract Ethylene/1‐olefin copolymers made with multiple‐site‐type catalytic systems typically have broad molecular weight distribution (MWD) and chemical composition distribution (CCD) because each site type produces polymer chains with distinct average chain microstructures. In this work, the simultaneous deconvolution of MWD and CCD was investigated to identify the number of site types and chain microstructures made on each site type. Four strategies based on different data sources were tested using the MWD and CCD simulated for an ethylene/1‐butene copolymer made with a catalyst having five site types. Our results indicate that the simultaneous deconvolution of the complete bivariate MWD and CCD is the best approach to describe the complete microstructure of the model ethylene/1‐butene copolymers. 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.227
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMacromolecular Reaction EngineeringSame topicMachine Learning in Materials ScienceFrench-language works237,207