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Record W2133888634 · doi:10.5555/2433508.2433903

Developing complex distributed simulation for industrial plant construction using high level architecture

2010· article· en· W2133888634 on OpenAlexaffabout
Simaan AbouRizk, Yasser Mohamed, Hosein Taghaddos, Farzaneh Saba, Stephen Hague

Bibliographic record

VenueWinter Simulation Conference · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReusabilityHigh-level architectureArchitectureDiscrete event simulationComputer scienceSystems engineeringProcess (computing)Event (particle physics)Distributed computingSoftware engineeringEngineeringSimulationOperating systemSoftware

Abstract

fetched live from OpenAlex

Large, complex construction projects, such as industrial plant construction, are not well suited to discrete-event process interaction simulation. The authors present a distributed simulation of industrial plant construction using separate modules inter-linked via the High Level Architecture (HLA). This enables the capture of all features, resources, and processes required to design, build, and maintain a facility, and an HLA-based approach simplifies collaborative development and improves reusability of components. The proposed methodology for simulating industrial construction is validated using COSYE, a Construction Synthetic Environment developed at the University of Alberta.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.533
GPT teacher head0.461
Teacher spread0.072 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2010
Admission routes2
Has abstractyes

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