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Record W3206103442 · doi:10.1145/3478384.3478422

Atmosphéries and the poetics of the in situ: the role and impact of sensors in data-to-sound transposition installations

2021· article· en· W3206103442 on OpenAlexaff
Anne Despond, Nicolas Reeves, Vincent Cusson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsPoeticsTransposition (logic)Sound (geography)Computer scienceAcousticsEnvironmental scienceArtPhysicsLiteratureArtificial intelligencePoetry

Abstract

fetched live from OpenAlex

Atmosphéries is a research-creation program which produced a series of art installations originally called “Cloud Harps”, that has known multiple transformations since their first instantiation in 1997. They are made of sculptural wooden “buffets” integrating different sensors that probe the sky and the surroundings to collect various atmospheric data. An internal process tuned by a human composer then maps these data to different audio parameters from a custom-made synthesis environment to create continuous sound and musical sequences. The present paper addresses conceptual and technological considerations about real-time sensing of clouds in a sonic-artistic installation context. Particular attention is given to the limitations and artefacts produced by such specialized sensors, and how those can be embraced to remain consistent with the spatio-temporal aspects of in situ works.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.022
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.299
Teacher spread0.278 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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