Training Specificity of Hurdle vs. Countermovement Jump Training
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".