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Record W2016198238 · doi:10.1590/permusi2015a3102

Gesto na performance da percussão, Parte 1: análise percentual de dados experimentais

2015· article· pt· W2016198238 on OpenAlexaff
Fernando Chaib, Homero Chaib Filho, João Catalão

Bibliographic record

VenuePer Musi · 2015
Typearticle
Languagept
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics

Abstract

fetched live from OpenAlex

Este artigo, Parte 1 do mesmo estudo, mostra como foi realizado um tratamento descritivo, através de uma análise percentual, dos dados experimentais obtidos ao observar a influência do gesto corporal sobre as sensações de continuidade, suspensão e conclusão de um trecho musical em percussão. Dentro da complexa relação estabelecida entre o percussionista e o texto musical apresentado em grande parte das produções para percussão, procuramos compreender até que ponto o corpo poderá ser um agente auxiliador no processo de transmissão de sensações específicas sobre a música executada em uma performance percussiva. A complexidade deste processo experimental nos obrigou a dividir os resultados obtidos em dois artigos. Este trabalho trata-se da 1ª parte de publicação dos dados adquiridos e analisados a partir dos pesos percentuais.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.064
GPT teacher head0.290
Teacher spread0.226 · 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.

Study designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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