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Record W2057300723 · doi:10.1109/mcg.2005.33

Applications of computers to dance

2005· article· en· W2057300723 on OpenAlexaff
Thomas Calvert, W. Wilke, Rhonda Ryman, Ilene Fox

Bibliographic record

VenueIEEE Computer Graphics and Applications · 2005
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of WaterlooCredo Interactive (Canada)Simon Fraser University
Fundersnot available
KeywordsDanceChoreographyComputer scienceComputer graphicsMultimediaVariety (cybernetics)Computer graphics (images)Human–computer interactionVisual artsArtificial intelligenceArt

Abstract

fetched live from OpenAlex

As computer technology has developed and become less expensive, many artists have found ways to use it to enhance their performances with interactive multimedia. This has included incorporating computer-generated images and sound with live dance performance and using sensors that let the live dancers' movements control imagery, sound, and a wide variety of special effects. This overview describes some of the current applications of computer graphics to dance including visualizing choreography, composing, editing and animating dance notation, and enhancing live performance.

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.006

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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
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

Citations107
Published2005
Admission routes1
Has abstractyes

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