THE NEUROMECHANICAL EFFECTS OF VARYING RELATIVE LOAD IN A MAXIMAL SQUAT JUMP
Bibliographic record
Abstract
Peak power occurs between 15–30% of maximal muscle force in mono-articular movements using small muscles. Conversely, in multi-joint movements requiring the recruitment of many large muscles, peak mechanical power is generated at higher fractions of peak force, usually between 50–70% of 1 repetition maximum (RM) and power typically is equivalent across a spectrum of loads. PURPOSE: To determine the influence of load on the kinematics and myoelectric manifestations of maximal squat jumps. METHODS: After determination of maximal squat strength, 5 active men and women (aged 21–37) performed maximal squat jumps at loads ranging from 20–80% of 1RM (mean, SD 142, 25 kg). Surface EMG of the m. vastus lateralis and m. biceps femoris was collected during each trial with root mean square and median frequency used to assess neural drive. An accelerometer and velocity tachometer was used to measure and calculate force, velocity and power exerted on the bar. RESULTS: Load had a substantial effect on force, velocity and power, and peak power (mean, SD 1928, 294 W) occurred across a spectrum of loads from 50–80% of 1RM. EMG was equivalent across loads indicating that neural drive was independent of load. CONCLUSIONS: The load used in squat jumping influences the mechanics of the movement but does not markedly effect muscle activation.
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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) | 0.002 | 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".