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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 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 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.

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: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.678

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 head0.009
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