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Record W2087960293 · doi:10.1145/1873951.1874221

Ozone

2010· article· en· W2087960293 on OpenAlexaff
Xin Wei Sha, Michael Fortin, Navid Navab, Timothy Sutton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsChoreographyImprovisationComputer scienceEvent (particle physics)GestureRange (aeronautics)Human–computer interactionSpace (punctuation)MultimediaDanceArtificial intelligenceVisual artsEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

This paper describes Ozone, a new media choreography system based on layered, continuous physical models, designed for building a diverse range of interactive spaces that coordinate arbitrary streams of video and audio synthesized in real-time response to continuous, concurrent activity by people in a live event. We aim to build rich responsive spaces that sustain the free improvisation of collectively or individually meaningful non-linguistic gesture. Ozone provides an expressive way to compose the potential "landscape" of an event evolving according to the designer's intent as well as contingent activity. A potential-energy engine evolves superposed states over simplicial complexes modeling the topological space of metaphorical states.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0690.016

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 head0.222
Teacher spread0.213 · 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
GenreOther

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

Citations8
Published2010
Admission routes1
Has abstractyes

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