MétaCan
Menu
Back to cohort
Record W2912497963

A Biomechanical Investigation of Warm-up Procedures for Musicians

2012· article· en· W2912497963 on OpenAlexaff
Donald Russell, Moyra McDill, Gilles Comeau, Nona Ahmadi

Bibliographic record

VenueCMBES Proceedings · 2012
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsContext (archaeology)Session (web analytics)Point (geometry)PsychologyApplied psychologyAffect (linguistics)Cognitive psychologyComputer scienceCommunicationHistory
DOInot available

Abstract

fetched live from OpenAlex

Published reports indicate that the rates of physical injury in musicians are surprisingly high and costly. One approach taken to minimize the risk of injury is the use of a proper warm-up prior to either a practice session or a performance. There is a lack of consensus among musicians about the nature and components of a proper warm-up. This paper will examine warm-up for musicians (primarily pianists) from a biomechanical point of view drawing first on information from the fields of performing arts medicine and treatises on music pedagogy to summarize the typical or recommended practices for musicians. The biomechanical facets of these practices are then analyzed to assess their affect the body in the context of the forces and movements required to play an instrument. As there is little research investigating the long term affects of warm-up practices on musicians’ health it is necessary to use results from analogous athletic activities to evaluate the efficacy of the components of warm-up procedures for musicians. As a result of bringing together and analyzing a range of data from a number of different activities we are able to suggest hypotheses regarding important components of warmup for musicians and the impacts of these activities. The results can serve as a basis for looking at long-term effects of warm-up on musicians’ health.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.439

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 studies0.0000.000
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 head0.051
GPT teacher head0.320
Teacher spread0.269 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCMBES ProceedingsSame topicMusicians’ Health and PerformanceFrench-language works237,207