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Record W2110126810 · doi:10.5555/2431518.2431944

Loosely coupled visualization of industrial construction simulation using a gaming engine

2011· article· en· W2110126810 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

VenueWinter Simulation Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVisualizationComputer sciencePipeline (software)Distributed Interactive SimulationInteractive simulationWindow (computing)Data visualizationVirtual realityDistributed computingHuman–computer interactionSimulationData miningWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

The use of simulation in construction project management is not widely adopted. Effective and intuitive tools and techniques to communicate simulation models with industry practitioners are needed. Visualization of simulation behaviors using three dimensional virtual worlds of the simulated construction operations is an effective medium of communication. However, developing visual behaviors to reflect hidden simulation behaviors is time consuming. The relatively small time window available for developing and using simulation models on real construction operations requires a time and cost effective approach for developing simulation driven visualization. This paper describes an approach that utilizes an open source gaming engine to develop parallel and loosely coupled simulation-driven visualizations of industrial construction operations in a distributed simulation environment. The paper focuses mainly on the development pipeline in a step-by-step approach to document and facilitate application of the same approach in similar simulations.

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 score0.602
Threshold uncertainty score0.767

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)0.0010.000

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 head0.089
GPT teacher head0.277
Teacher spread0.189 · 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