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Record W1988185176 · doi:10.1021/ie048957o

Mathematical Model and Parameter Estimation for Gas-Phase Ethylene Homopolymerization with Supported Metallocene Catalyst

2005· article· en· W1988185176 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

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsQueen's University
Fundersnot available
KeywordsMetalloceneEthyleneEstimation theoryPost-metallocene catalystWork (physics)Experimental dataPhase (matter)Materials scienceCatalysisBiological systemThermodynamicsPolymerizationChemistryComputer scienceMathematicsAlgorithmStatisticsOrganic chemistryPhysicsPolymer

Abstract

fetched live from OpenAlex

A dynamic model for gas-phase ethylene homopolymerization using a supported metallocene catalyst is developed in this work. A single-site model was first developed, with model parameters estimated from measurements from 11 experimental runs in a semibatch laboratory-scale reactor. Estimability analysis techniques were applied to aid in the parameter estimation. Although the single-site model provided good fits for both the polymerization rate and hydrogen concentration, it failed to accurately predict the molecular weight data and its distribution. Sequentially, a simplified two-site model was built to improve model predictions. The two-site model, which used three additional parameters, showed significant improvements over the single-site model. The two-site model was validated using the data from two extra experimental runs, which were not employed in the parameter estimation process. Most of the model predictions fall within the 95% confidence intervals of the experimental data.

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)0.001
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 score0.399
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.094
GPT teacher head0.341
Teacher spread0.247 · 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