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NEW REALITIES FOR CANADA’S PARLIAMENT:A WORKFLOW FOR PREPARING HERITAGE BIM FOR GAME ENGINES AND VIRTUAL REALITY

2019· article· en· W2970671956 on OpenAlexaffabout
Cailen Pybus, Katie Graham, Joey Doherty, N. Arellano, Stephen Fai

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsCarleton University
Fundersnot available
KeywordsStorytellingDocumentationVirtual realityWorkflowCultural heritagePerformative utteranceMultimediaComputer scienceWorld Wide WebParliamentContent creationNarrativeHuman–computer interactionPolitical scienceArtDatabase

Abstract

fetched live from OpenAlex

Abstract. With a growing interest in the use of virtual reality (VR) for dissemination of cultural heritage sites, the question of how to leverage existing documentation as content for virtual experiences becomes a potentially valuable opportunity. Notably, as sites are increasingly documented with building information modelling (BIM) for the purposes of conservation, there is potential to give these models a second life as content for public education and promotion. However, although software exist for viewing BIM in VR headsets, they are inadequate for complex models typical of heritage buildings, and lack functionality for integrating custom didactic content and storytelling. To make BIM performative in VR and allow for custom content, a workflow was developed to translate BIM into game engine scenes — which optimizes geometry following performance guidelines of VR while maintaining the high visual fidelity of the BIM. As a case study, six heritage spaces of the Centre Block of the Canadian Parliament which had been previously documented and modelled by CIMS were prepared for Unity3D, enabling their later use in a storytelling experience.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations34
Published2019
Admission routes2
Has abstractyes

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