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Record W4205185215 · doi:10.1121/2.0001007

How different strings affect violin qualities

2018· article· en· W4205185215 on OpenAlexaffabout

Bibliographic record

VenueProceedings of meetings on acoustics · 2018
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsViolinSignificant differencePerceptionSet (abstract data type)Test (biology)

Abstract

fetched live from OpenAlex

The brand and model of strings used on violins are considered to play a significant role in their playability and sound quality. An experiment was designed to test the perceptual quality of different violin strings. A professional violinist selected two violins, from a set of the same make/model, that had similar sound and playing qualities. Three different types of strings were chosen for this study: Dominant, Kaplan and Pro-Arté strings. Professional and advanced student violinists were invited to play and evaluate the violins. Two violins were both strung with Dominant strings initially (session labelled D1-D2). Subjects rated the difference between the two violins (violin 2 compared to violin 1) according to eight criteria. Then the strings of violin 2 were changed to a different type. Subjects rated the difference between the two violins again. In Oberlin, subjects compared Dominant and Kaplan strings in two sessions (called respectively D1-D2 and D1-K2). In Montreal, subjects compared Dominant, Kaplan and Pro-Arté strings in three trials (D1-D2, D1-K2 and D1-P2). Results showed no significant differences between the experiment conditions in either place except that the brightness difference ratings in D1-D2 were found to be significantly higher than in D1-P2 based on the Montreal results.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.020
GPT teacher head0.249
Teacher spread0.230 · 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 designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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