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Record W2167755910 · doi:10.1109/vecims.2008.4592752

Supporting web-based e-learning through collaborative virtual environments for radiotherapy treatment: A formal design

2008· article· en· W2167755910 on OpenAlexaff
Abdulaziz Al Hamidi, Azzedine Boukerche, Luqman Ahmad, Ming Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceDEVSVirtual realityWeb applicationInstructional simulationHuman–computer interactionClass (philosophy)Formal specificationMultimediaVirtual learning environmentWeb modelingWorld Wide WebWeb serviceSoftware engineeringModeling and simulationSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

Web based virtual reality application has been widely used in education, virtual conferences, entertainment. Observing the progress of the gaming industry over the years can clearly show the advancement in virtual reality Web application and virtual environments. In this paper, we propose a novel Web based e-learning application design approach that uses discrete event system specification (DEVS) formalism to form and model the 3D virtual hospital Web based application for radiotherapy and cancer care treatment class of applications. We present a case study that demonstrates the value of this formal approach in modeling and designing 3D virtual environment and Web based e-learning class of 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 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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.278
Teacher spread0.249 · 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 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

Citations5
Published2008
Admission routes1
Has abstractyes

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