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Record W2080058093 · doi:10.1145/1101149.1101276

The dancing genome project

2005· article· en· W2080058093 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChoreographyComputer scienceMovement (music)Process (computing)Motion captureConvergence (economics)Genetic algorithmSimple (philosophy)Rate of convergenceVocabularyArtificial intelligenceMotion (physics)Human–computer interactionComputer visionDanceComputer graphics (images)Machine learningProgramming languageKey (lock)

Abstract

fetched live from OpenAlex

In this paper, we present an interactive genetic algorithm for the generation of human-computer choreography, using motion capture technology. First, we introduce the four steps of the algorithm to (1) define a movement vocabulary, (2) initialize movement sequences, (3) generate mutants, and (4) select mutant sequences to create a choreography. Then, we show how this approach is implemented in real time to create interaction among dancers. Finally, we run simulations to assess the convergence rate of the algorithm, before generating a simple duet for actual and virtual dancers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.797
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations19
Published2005
Admission routes2
Has abstractyes

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