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Record W2297885137 · doi:10.1021/acs.macromol.5b02755

Quantifying the Copolymerization Kinetics of Ethylene and 1-Octene Catalyzed with <i>rac</i>-Et(Ind)<sub>2</sub>ZrCl<sub>2</sub> in a Solution Reactor

2016· article· en· W2297885137 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

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEthyleneCopolymerKinetics1-OcteneCatalysisMonomerPolymerizationPolymer chemistryChemistryReactivity (psychology)Reaction rate constantThermodynamicsPhysical chemistryMaterials sciencePolymerOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

We developed a new methodology to estimate the cross-propagation rate constants and their confidence regions for the copolymerization of ethylene and 1-olefins using single site catalysts. We applied the method to a series of ethylene/1-octene copolymers made with rac -Et(Ind) 2 ZrCl 2 /MAO in a solution reactor operated in semibatch mode. The method estimates the reactivity ratios using the Mayo–Lewis equation and the cross-propagation rate constants with combination of the Mayo–Lewis equation and a polymerization kinetics model based on monomer uptake curves. More importantly, our strict statistical treatment of the data allows us to estimate the joint confidence regions for these parameters, which is essential to establish the reliability of these estimates.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.732

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 studies0.0000.001
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 head0.013
GPT teacher head0.245
Teacher spread0.231 · 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