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Record W2135799075 · doi:10.1002/mats.201500017

Reactivity Ratio Estimation in Non‐Linear Polymerization Models using Markov Chain Monte Carlo Techniques and an Error‐In‐Variables Framework

2015· article· en· W2135799075 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecular Theory and Simulations · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMarkov chain Monte CarloMonte Carlo methodMarkov chainApplied mathematicsLinear regressionComputer scienceNonlinear regressionMathematicsRegression analysisMathematical optimizationStatistics

Abstract

fetched live from OpenAlex

Reactivity ratio estimation was carried out in various nonlinear models using Markov Chain Monte Carlo (MCMC) technique and an error‐in‐variables (EVM) regression model. The implementation steps for three different polymerization case studies are discussed in detail and the results from this work are compared to previously used approximation methods. Approximation techniques that rely on linear regression theory are shown to produce inaccurate joint confidence regions (JCRs). Therefore, in this paper, we explore MCMC techniques that can be used to produce JCRs with correct shape and probability content. In addition, the paper illustrates how an EVM model can be used in tackling any type of regression problem, including multi‐response problems.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.355
Teacher spread0.291 · 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