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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 OpenAlexaff
Divyarajsinh M. Solanki, Hrushikesh LADDHA, Muhammed Zain Kangda, Ehsan Noroozinejad Farsangi

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.

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)0.003
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0090.008
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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

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
GenreReview

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

Citations7
Published2023
Admission routes1
Has abstractyes

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