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Changes in kinematics and trunk electromyography during a 2000 m race simulation in elite female rowers

2010· article· en· W1597950615 on OpenAlexafffund
Courtney L. Pollock, I. Chester Jones, Thomas R. Jenkyn, Tanya D. Ivanova, S. Jayne Garland

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2010
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrunkKinematicsElectromyographyPhysical medicine and rehabilitationMedicineTorsoUpper trunkAnatomyMotor controlRowingPhysicsBiology

Abstract

fetched live from OpenAlex

Achieving excellence in rowing requires optimization of technique to maximize efficiency and force production. Investigation of the kinematics of the trunk, upper and lower extremity, together with muscle activity of the trunk, provides an insight into the motor control strategies utilized over a typical race. Nine elite female rowers performed a 2000 m race simulation. Kinematic data of the trunk and extremities, together with electromyography (EMG) activity of spinal and pelvic extensor and flexor muscles, were compared at 250 and 1500 m. At 1500 m, there was greater dissociation in the timing of leg extension and arm flexion and delayed trunk extension. Also at 1500 m, the spine demonstrated a delayed peak extension angular velocity of the T4-T7 and L3-S1 spinal segments in the early drive along with delayed and increased peak extension angular velocity of T10-L1 and L1-L3 spinal segments during the late drive. Trunk muscle fatigue was not evident; however, the abdominals demonstrated larger EMG burst areas at 1500 m. Alterations in trunk kinematics suggest that the trunk acts as a less stiff lever on which to transfer the forces of the legs to the arms and handle. Increased abdominal activity may reflect increased demand to control the trunk, given the altered coordination between the legs, trunk and arms.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.014
GPT teacher head0.301
Teacher spread0.287 · 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 designObservational
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

Citations38
Published2010
Admission routes2
Has abstractyes

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