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Record W4383477629 · doi:10.59440/ceer-2023-0002

AUGMENTED AND VIRTUAL REALITIES: THE FUTURE OF BUILDING DESIGN AND VISUALIZATION

2023· article· en· W4383477629 on OpenAlex
Divyarajsinh M. Solanki, Hrushikesh LADDHA, Muhammed Zain Kangda, Ehsan Noroozinejad Farsangi

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

VenueCivil And Environmental Engineering Reports · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReal estateVisualizationVirtual realityProcess (computing)ArchitectureArchitectural engineeringComputer scienceAugmented realityEngineering managementEngineeringHuman–computer interactionBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

The present study precisely conveys the methodology of developing a three-dimensional (3D) architectural model of a villa with its walk-through and displaying the model in virtual reality, which as a result, be used by the clients to spectate, customize and buy the real estate property. Additionally, the case study highlights the advancement in architecture, as certain specifications of each element of a 3D model can be viewed in a virtual environment. Virtual reality is a transpiring platform, and in addition to that, the real-estate sector shows its incorporation in designing, marketing, and selling projects. The teaching and learning process can be eased out by intervening it with technology that generates an enhanced visualization environment. These technologies, when used constructively, save time and energy and also hoard economic standards ensuing lucrative benefits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.206

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 communication0.0000.000
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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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