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Record W2068310249 · doi:10.1519/jsc.0b013e318208d43c

Training Specificity of Hurdle vs. Countermovement Jump Training

2011· article· en· W2068310249 on OpenAlexaff
Darío F Cappa, David G. Behm

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsJumpMathematicsCountermovementPhysical medicine and rehabilitationJumpingOrthodonticsMedicinePhysics

Abstract

fetched live from OpenAlex

The objective of this study was to compare bilateral and unilateral hurdle jumps with traditional countermovement jumps (CMJs). Thirteen athletes were tested during continuous forward bilateral and unilateral hurdle jumps and single CMJ. Countermovement jump height was used to establish the hurdle height. Subjects jumped forward over 4 hurdles with the force plate positioned after the second hurdle to measure vertical ground reaction force (VGRF), contact time (CT), and rate of force development (RFD). For bilateral jumps, hurdle height was established at maximal (100%) CMJ height and at 120, 140, and 160% of the CMJ height. The athletes were instructed to jump as fast as possible to mimic a training session drill. For unilateral jumps, hurdle height was set at 70, 80, and 90% of the CMJ height. Bilateral 160% jumps showed a significantly longer CT than bilateral 100, 120, and 140% jumps. The bilateral 100, 120, and 140% jumps had significantly shorter CT than the unilateral jumps and CMJ. The VGRF during bilateral jumps was higher than unilateral jumps and CMJ. Bilateral 160% jump RFD was significantly higher than CMJ and unilateral jumps but significantly lower than the other bilateral jumps. In conclusion, the characteristics of the bilateral jumps were substantially different from those of the CMJ and unilateral hurdle jumps. As bilateral hurdle jumps with a height between 100 and 140% of the CMJ provide similar CTs and VGRF as many reported sprint or jump actions, they may be considered a more training-specific power training drill than the CMJ.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.168
GPT teacher head0.365
Teacher spread0.197 · 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

Citations32
Published2011
Admission routes1
Has abstractyes

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