MétaCan
Menu
Back to cohort
Record W2974850545 · doi:10.1162/lmj_a_01057

Respire: Virtual Reality Art with Musical Agent Guided by Respiratory Interaction

2019· article· en· W2974850545 on OpenAlex
Kıvanç Tatar, Mirjana Prpa, Philippe Pasquier

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

VenueLeonardo Music Journal · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsArtificial Intelligence in Medicine (Canada)Simon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceBreathingHuman–computer interactionWearable computerPoint (geometry)Augmented realityEmbodied cognitionMusicalVirtual realityArtificial intelligenceBiologyAnatomyArtVisual artsMathematics

Abstract

fetched live from OpenAlex

Respire is an immersive art piece that brings together three components: an immersive virtual reality (VR) environment, embodied interaction (via a breathing sensor) and a musical agent system to generate unique experiences of augmented breathing. The breathing sensor controls the user’s vertical elevation of the point of view under and over the virtual ocean. The frequency and patterns of breathing data guide the arousal of the musical agent, and the waviness of a virtual ocean in the environment. Respire proposes an intimate exploration of breathing through an intelligent mapping of breathing data to the parameters of visual and sonic environments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.989

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.032
GPT teacher head0.236
Teacher spread0.204 · 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