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Record W4392628986 · doi:10.26868/25222708.2023.1397

A bibliometric analysis of immersive virtual environment applications for occupant comfort and behavior research

2023· article· en· W4392628986 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

VenueBuilding Simulation Conference proceedings · 2023
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceVirtual realityDomain (mathematical analysis)Human–computer interactionEnergy consumptionArchitectural engineeringPerceptionData scienceEngineeringPsychology

Abstract

fetched live from OpenAlex

A bibliometric analysis is conducted to systematically analyze research trends and opportunities in Virtual Reality (VR) and Immersive Virtual Environments (IVEs) applications to study indoor occupant comfort and behaviors in buildings. The methodology includes: (i) article search and selection; (ii) analysis of sources; and (iii) network analysis and mapping. Results show that research activities remain limited to a few institutions and researchers, with limited global representation. In addition, research trends in the past two decades evolved from focusing on fire safety to energy consumption, occupant perceptions, comfort, and behavior. Future research directions include multi-domain evaluations of occupant-building interactions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0310.080
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 head0.143
GPT teacher head0.417
Teacher spread0.274 · 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