MétaCan
Menu
Back to cohort
Record W4367662895 · doi:10.1109/vrw58643.2023.00123

Designing Prototype XRI Workspaces with Mixed Reality and IoT Devices for Immersive Adaptive Environments

2023· article· en· W4367662895 on OpenAlexafffund
Jigyasa Agarwal, Alexis Morris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsOntario College of Art and Design
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsWorkspaceComputer scienceHuman–computer interactionInternet of ThingsEmbodied cognitionFocus (optics)Mixed realityContext (archaeology)Domain (mathematical analysis)ArchitectureProcess (computing)Virtual realityMultimediaWorld Wide WebRobotArtificial intelligence

Abstract

fetched live from OpenAlex

Mixed Reality (XR) and Internet-of-Things (IoT) are converging paradigms which are evolving into the metaverse. The XRI research domain addresses these hybrid IoT and XR ecosystems with a focus on embodied environmental objects and avatars. This work contributes the results of an XRI design rapid prototype process. Specifically, it presents i) an XRI system architecture, ii) a context-driven workspace prototype, and iii) an early design-science evaluation, toward new immersive adaptive spaces.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.045
GPT teacher head0.274
Teacher spread0.230 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicAugmented Reality ApplicationsFrench-language works237,207