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Record W2346417716 · doi:10.1177/0037549715611485

Modelling and simulation of complex cellular models using Cell-DEVS

2015· article· en· W2346417716 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

VenueSIMULATION · 2015
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCellular automatonExecutableDEVSComputer scienceFormalism (music)Complex systemDiscrete event simulationTheoretical computer scienceAutomatonDistributed computingModeling and simulationAlgorithmProgramming languageSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

Cellular automata are discrete dynamical systems that provide a mathematical framework for modelling, studying and predicting the behaviour and response of systems across many different disciplines and domains, ranging from physical and biological to computational and social models. Cell-DEVS is a formalism that provides a discrete event approach to define cellular models with timing delay constructions and using simple definition of complex timing. It has been shown that the application of the Cell-DEVS paradigm produces a significant reduction in the development times of cell-shaped models and a wide variety of complex models has been developed using this approach. In this work we present the definition of complex cellular automata models using the Cell-DEVS paradigm, we use the CD++ tool to obtain executable models and study their behaviour through computer simulation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.402

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 communication0.0000.001
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 head0.158
GPT teacher head0.309
Teacher spread0.151 · 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