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Record W4391616684 · doi:10.32920/25164572

THEAT[AR]: Augmented Reality Based Pre-visualization Of Theatrical Design Elements

2024· preprint· en· W4391616684 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSpreadsheets and End-User Computing
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAugmented realityVisualizationComputer sciencePoint (geometry)Human–computer interactionSpace (punctuation)Process (computing)MultimediaMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

<p>Theatrical Design is often a very fluid medium, changing until the very last minute, or even second. Even then, with all the care and time put into a production, once constructed on stage the end result often looks different than one might have imagined. Augmented Reality pre-visualization allows us to see theatrical designs in real spaces, as well as work with other designers to collaborate and compare elements for a more cohesive design. Up until this point pre-visualization has been popular, but most programs have not been designed for more than one type of design, and none have made use of Augmented Reality. This technology allows designers to communicate visually with other members of the creative team in a more definite manner, and allows for more confidence when entering the theatre or performance space. In turn this will save productions time and money, and allow for a better creative process, resulting in better art.</p>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.066
GPT teacher head0.338
Teacher spread0.272 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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