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Record W2405087042 · doi:10.1145/2851581.2891093

Avian Attractor

2016· article· en· W2405087042 on OpenAlexaff
Judith Doyle, Naoto Hieda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsMcGill UniversityOntario College of Art and Design
Fundersnot available
KeywordsComputer scienceMerge (version control)Computer graphics (images)AttractorComputer visionFlocking (texture)Point cloudArtificial intelligenceHuman–computer interactionVisual artsMathematics

Abstract

fetched live from OpenAlex

Avian Attractor is a gestural projection combining depth images of viewers and pre-captured shots of birds in natural and architectural environment. Surface impressions of viewers merge with those of urban birds and procedural agents that extend their flight paths and trajectories. These moving images are both seen and seen through -- motionscapes that combine figurative elements with cross-hatchings, tendrils and flocking agents. The Avian Attractor installation is supported by other art research activity that aims to provide tools for a diversity of users without programming skills or collaborators. This includes development of a point cloud camera-recorder and interface for non-programmers. Inspired by a bird feeder in a cold city, Avian Attractor uses off-the-shelf depth cameras and projection to generate a hybrid form of space where post-human embodiment can be explored and expanded.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.318

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0950.019

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 head0.024
GPT teacher head0.200
Teacher spread0.176 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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