MétaCan
Menu
Back to cohort
Record W2111623497 · doi:10.1080/02640414.2014.908320

A biomechanical comparison of the vertical jump, power clean, and jump squat

2014· article· en· W2111623497 on OpenAlexaff
Sasho MacKenzie, Robert J. Lavers, Brendan B. Wallace

Bibliographic record

VenueJournal of Sports Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsJumpGround reaction forceKinematicsCountermovementElectromyographySquatBicepsVastus medialisAnklePhysical medicine and rehabilitationForce platformVertical jumpMathematicsPower (physics)MedicinePhysicsAnatomyClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the kinetics, kinematics, and muscle activation patterns of the countermovement jump, the power clean, and the jump squat with the expectation of gaining a better understanding of the mechanism of transfer from the power clean to the vertical jump. Ground reaction forces, electromyography, and joint angle data were collected from 20 trained participants while they performed the three movements. Relative to the power clean, the kinematics of the jump squat were more similar to those of the countermovement jump. The order in which the ankle, knee, and hip began extending, as well as the subsequent pattern of extension, was different between the power clean and countermovement jump. The electromyography data demonstrated significant differences in the relative timing of peak activations in all muscles, the maximum activation of the rectus femoris and biceps femoris, and in the activation/deactivation patterns of the vastus medialis and rectus femoris. The greatest rate of force development during the upward phase of these exercises was generated during the power clean (17,254 [Formula: see text]), which was significantly greater than both the countermovement jump (3836 [Formula: see text]) and jump squat (3517 [Formula: see text]) conditions (P < .001, [Formula: see text]).

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.170

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 studies0.0000.000
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 head0.032
GPT teacher head0.326
Teacher spread0.293 · 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

Citations62
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sports SciencesSame topicSports Performance and TrainingFrench-language works237,207