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Record W2767226973

Tuning to Trust: System Calibration as Creative Enabler

2017· article· en· W2767226973 on OpenAlexfundno aff
Pierre Alexandre Tremblay

Bibliographic record

VenueUniversity of Huddersfield Repository (University of Huddersfield) · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersCanada Council for the ArtsCentre for Interdisciplinary Research in Music Media and Technology
KeywordsEnablingSoftware portabilityAdaptabilityComputer scienceSet (abstract data type)CalibrationField (mathematics)Quality (philosophy)Human–computer interactionMultimediaPsychologyManagement
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a mixed-music composition methodology emerging from the author’s latest practice-based research in the field over the last five years. The calibration of the interactive performance systems has enabled trust in reproducible sound quality for both the composer and the performers, enhancing the portability and adaptability of the works, and permitting increasingly daring creative experiments without compromising the rehearsal and concert experiences. A set of general, transferable responsibilities and solutions are presented and assessed against clear design criteria in the author’s latest pieces.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
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 communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.186
Teacher spread0.174 · 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
Published2017
Admission routes1
Has abstractyes

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