Effects of drop height and surface instability on neuromuscular activation during drop jumps
Bibliographic record
Abstract
The purpose of this study was to examine whether drop height-induced changes in leg muscle activity during drop jumps (DJ) are additionally modulated by surface condition. Twenty-four healthy participants (23.7 ± 1.8 years) performed DJs on a force plate on stable, unstable, and highly unstable surfaces using different drop heights (i.e., 20 cm, 40 cm, 60 cm). Electromyographic (EMG) activity of soleus (SOL), gastrocnemius (GM), tibialis anterior (TA) muscles and coactivation of TA/SOL and TA/GM were analyzed for time intervals 100 ms prior to ground contact (preactivation) and 30-60 ms after ground contact [short latency response (SLR)]. Increasing drop heights resulted in progressively increased SOL and GM activity during preactivation and SLR (P < 0.01; 1.01 ≤ d ≤ 5.34) while TA/SOL coactivation decreased (P < 0.05; 0.51 ≤ d ≤ 3.01). Increasing surface instability produced decreased activities during preactivation (GM) and SLR (GM, SOL) (P < 0.05; 1.36 ≤ d ≤ 4.30). Coactivation increased during SLR (P < 0.05; 1.50 ≤ d ≤ 2.58). A significant drop height × surface interaction was observed for SOL during SLR. Lower SOL activity was found on unstable compared to stable surfaces for drop heights ≥40 cm (P < 0.05; 1.25 ≤ d ≤ 2.12). Findings revealed that instability-related changes in activity of selected leg muscles are minimally affected by drop height.
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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 | 0.000 | 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".