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Record W2046302356 · doi:10.1002/mren.201100053

The Integrated Deconvolution Estimation Model: Estimation of Reactivity Ratios per Site Type for Ethylene/1‐Butene Copolymers Made with a Heterogeneous Ziegler‐Natta Catalyst

2011· article· en· W2046302356 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
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReactivity (psychology)EthyleneCopolymerNattaDeconvolutionCatalysisOlefin fiberPolymer chemistryChemistryMaterials scienceButenePolymerizationOrganic chemistryPolymerMathematicsAlgorithm

Abstract

fetched live from OpenAlex

Abstract The integrated deconvolution estimation model (IDEM) can be used to estimate the reactivity ratios of multiple‐site‐type catalysts used to make ethylene/α‐olefin copolymers, such as heterogeneous Ziegler‐Natta and Phillips catalysts. The estimation process combines high‐temperature GPC and 13 C NMR data to find the reactivity ratios per site type. The IDEM is applied to two sets of ethylene/1‐butene copolymer samples made with an industrial TiCl 4 /MgCl 2 catalyst in the presence and absence of hydrogen. A sensitivity analysis for parameter estimation is developed and the effect of the presence of hydrogen on the reactivity ratio per site type is quantified for the first time for this copolymerization system. 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.776

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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