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Record W2133325186 · doi:10.1109/vecims.2008.4592764

AVRA: a Architecture for VR-based Applications

2008· article· en· W2133325186 on OpenAlexaff
Saeid Nourian, Nicolas D. Georganas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceXMLConstruct (python library)Focus (optics)Virtual realityArchitectureTask (project management)Human–computer interactionPlug-inProgramming languageWorld Wide WebSystems engineering

Abstract

fetched live from OpenAlex

The most fundamental ingredients of computer simulations are the analytical or experimental models and their visual representations (views). This paper presents AVRA, a framework that receives the simulation models and the description of the view as XML files and dynamically generates a VR (Virtual Reality) application that corresponds to those specifications. The result consists of a 3D scene with configurable graphical elements that are animated based on the numerical outputs of the simulation models. The task of managing the communication between the model and view components as well as their construction and destruction is automatically handled by AVRA. In essence, this framework allows developers to quickly construct the simulation components of a VR application through XML descriptions and view plug-ins thereby allowing developers to focus their efforts on implementing the higher level functionalities of the application.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.195
GPT teacher head0.443
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2008
Admission routes1
Has abstractyes

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