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Record W4237454264 · doi:10.1162/leon.2007.40.3.248

<i>SwarmArt</i>: Interactive Art from Swarm Intelligence

2007· article· en· W4237454264 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.

Bibliographic record

VenueLeonardo · 2007
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSwarm behaviourMultitudeContext (archaeology)ExhibitionComputer scienceSwarm intelligenceHuman–computer interactionSwarm roboticsCollective intelligenceVariety (cybernetics)MultimediaData scienceArtificial intelligenceVisual artsBiologyArtParticle swarm optimizationEpistemology

Abstract

fetched live from OpenAlex

Swarms of bees, colonies of ants, schools of fish, flocks of birds, and fireflies flashing synchronously are all examples of highly coordinated behaviors that emerge from collective, decentralized intelligence. Local interactions among a multitude of agents or “swarmettes” lead to a variety of dynamic patterns that may seem like choreographed movements of a meta-organism. This paper describes SwarmArt, a collaborative project between several computer scientists and an artist, which resulted in interactive installations that explore and incorporate basic mechanisms of swarm intelligence. The authors describe the scientific context of the artwork, how user interaction is provided through video surveillance technology, and how the swarm-based simulations were implemented at exhibitions and galleries.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.258
Teacher spread0.245 · 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