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Record W3111347667 · doi:10.1162/leon_a_02009

Narcissus and Echo:<i>Reflections on an Art-Science Collaboration</i>

2020· article· en· W3111347667 on OpenAlexaff
D.A. Steinman, Peter Coppin, David A. Steinman

Bibliographic record

VenueLeonardo · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsOntario College of Art and DesignUniversity of Toronto
Fundersnot available
KeywordsThe artsVisual artsSonificationDigital artGeneral partnershipArtComputer scienceMultimediaCognitive scienceArt historyHuman–computer interactionPsychologyPerformance art

Abstract

fetched live from OpenAlex

Abstract More than a decade ago, the authors proposed establishing a basis for scientific exploration of blood-flow dynamics intertwined with the visual arts. Here they present a case study showing how paradigms they codeveloped for visually abstracting cerebral aneurysm blood flows were extrapolated to sonification and bimodal representations, and how a close interdisciplinary partnership was effected by guiding engineering students versed in the arts and artists adept with digital technology toward final outcomes greater than the sum of their parts.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.222

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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