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Record W2395815217 · doi:10.5281/zenodo.1178631

Rainboard And Musix: Building Dynamic Isomorphic Interfaces

2013· article· en· W2395815217 on OpenAlexaff
Brett Park, David Gerhard

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2013
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsVariety (cybernetics)Computer scienceIsomorphism (crystallography)Human–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

"Since Euler's development of the Tonnetz in 1739, musicians, composers andinstrument designers have been fascinated with the concept of musicalisomorphism, the idea that by arranging tones by their harmonic relationshipsrather than by their physical properties, the common shapes of musicalconstructs will appear, facilitating learning and new ways of exploringharmonic spaces. The construction of isomorphic instruments, beyond limitedsquare isomorphisms present in many stringed instruments, has been a challengein the past for two reasons: The first problem, that of re-arranging noteactuators from their sounding elements, has been solved by digital instrumentdesign. The second, more conceptual problem, is that only a single isomorphismcan be designed for any one instrument, requiring the instrument designer (aswell as composer and performer) to ""lock in"" to a single isomorphism, or tohave a different instrument for each isomorphism in order to experiment. Musix(an iOS application) and Rainboard (a physical device) are two new musicalinstruments built to overcome this and other limitations of existing isomorphicinstruments. Musix was developed to allow experimentation with a wide varietyof different isomorphic layouts, to assess the advantages and disadvantages ofeach. The Rainboard consists of a hexagonal array of arcade buttons embeddedwith RGB-LEDs, which are used to indicate characteristics of the isomorphismcurrently in use on the Rainboard. The creation of these two instruments /experimentation platforms allows for isomorphic layouts to be explored in waysthat are not possible with existing instruments."

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
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 studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.020
GPT teacher head0.228
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicMusic Technology and Sound StudiesFrench-language works237,207