MétaCan
Menu
Back to cohort
Record W2070440089 · doi:10.1121/1.3203209

The player and the bowed string: Coordination of bowing parameters in violin and viola performance

2009· article· en· W2070440089 on OpenAlex
Erwin Schoonderwaldt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
FundersSchulich School of Music
KeywordsViolaViolinBowingString (physics)Computer scienceAcousticsPhysicsTheoretical physicsCommunicationPsychology

Abstract

fetched live from OpenAlex

An experiment was conducted with four violin and viola players, measuring their bowing performance using an optical motion capture system and sensors on the bow. The measurements allowed for a detailed analysis of the use and coordination of the main bowing parameters bow velocity, bow force, and bow-bridge distance. An analysis of bowing strategies in detache playing of notes of three durations (0.2, 2, and 4 s) at three dynamic levels (pp, mf, and f) on all four strings is presented, focusing on the "steady" part of the notes. The results revealed clear trends in the coordinated variations of the bowing parameters depending on the constraints of the task, reflecting a common behavior as well as individual strategies. Furthermore, there were clear indications that the players adapted the bowing parameters to the physical properties of the string and the instrument, respecting the limits of the playable control parameter space.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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