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Record W2394736302 · doi:10.5281/zenodo.1178336

Musician Assistance And Score Distribution (Masd)

2012· article· en· W2394736302 on OpenAlexaff
Nathan Magnus, David Gerhard

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsConfusionComputer scienceRendering (computer graphics)PorcupineThe InternetArtificial intelligenceWorld Wide WebPsychology

Abstract

fetched live from OpenAlex

The purpose of the Musician Assistance and Score Distribution (MASD) system is to assist novice musicians with playing in an orchestra, concert band, choir or other musical ensemble. MASD helps novice musicians in three ways. It removes the confusion that results from page turns, aides a musician's return to the proper location in the music score after the looking at the conductor and notifies musicians of conductor instructions. MASD is currently verified by evaluating the time between sending beats or conductor information and this information being rendered for the musician. Future work includes user testing of this system. There are three major components to the MASD system. These components are Score Distribution, Score Rendering and Information Distribution. Score Distribution passes score information to clients and is facilitated by the Internet Communication Engine (ICE). Score Rendering uses the GUIDO Library to display the musical score. Information Distribution uses ICE and the IceStorm service to pass beat and instruction information to musicians.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.074
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0740.035

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 head0.039
GPT teacher head0.236
Teacher spread0.196 · 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 designSimulation or modeling
Domainnot available
GenreSoftware

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

Citations3
Published2012
Admission routes1
Has abstractyes

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