Neuromuscular Characteristics of Drop and Hurdle Jumps With Different Types of Landings
Bibliographic record
Abstract
The objective of this study was to compare drop (DJ) and hurdle jumps using a preferred, flat foot (FLAT) and forefoot (FORE) landing technique. Countermovement jump height was used to establish the hurdle and the DJ heights. The subjects performed forward hurdles and vertical DJs on a force plate. Measures included vertical ground reaction force (VGRF), contact time, leg stiffness, and rate of force development (RFD). Electromyographic (EMG) activity was measured in the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius during 3 phases: preactivity, eccentric phase, and concentric phase. All the kinetic variables favored hurdles over DJs. Specifically, hurdle-preferred technique and FORE exhibited the shortest contact time and DJ FLAT the longest. The VGRF was higher in hurdle preferred and FORE than in DJ preferred, FLAT, and FORE. For stiffness and RFD, hurdle preferred and FORE were higher than DJ preferred and FLAT. Hurdle jumps showed higher rectus femoris EMG activity than DJ did during preactivity and eccentric phases but lower activity during the concentric phase. Considering the type of landing, FLAT generally demonstrated the greatest EMG activity. During the concentric phase, DJ exhibited higher rectus femoris EMG activity. Biceps femoris activity was higher with hurdles in all the phases. Gastrocnemius showed the highest EMG activity during the concentric phase, and during the eccentric phase, hurdle preferred and FORE showed the highest results. In conclusion, the hurdle FORE technique was the most powerful type of jump.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".