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Record W7092287958 · doi:10.1177/20592043251379671

Stabilizing Joint Angle Velocity Contributes to Motor Learning in Percussion

2025· article· en· W7092287958 on OpenAlex

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

VenueMusic & Science · 2025
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity of Toronto
FundersCanada Foundation for Innovation
KeywordsElbowMotor learningWristPercussionTask (project management)Angular velocityMotion (physics)Elbow flexion

Abstract

fetched live from OpenAlex

This study quantified movement changes in the upper-limb joints during motor learning in percussion performance. Eleven percussionists practiced a novel excerpt on 3 consecutive training days, followed 1 week later by a retention assessment. Motion capture technology quantified the orientation and angular velocity of the shoulders, elbows, and wrists. To manipulate task difficulty, and thus the behavioral demands on upper-limb movements, participants learned the test excerpt in fast and slow tempo conditions. Given previous studies examining motor performance in percussion, it was hypothesized that variability in elbow and wrist angular velocity would be altered by performance tempo but would stabilize when comparing training vs. retention performances. The results showed greater abduction of the left shoulder in the slow vs. fast training condition. Variability in the velocity of right elbow and left wrist movements was lower at retention vs. training. Judges’ ratings of the performances revealed improved quality and rhythmic accuracy at retention vs. training. The findings may suggest that angular velocity mechanistically reflects motor learning in percussion performance. Such findings may be applied towards enhancing percussion training.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.316
Teacher spread0.282 · 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