A Mechanics Comparison Between Landing From a Countermovement Jump and Landing From Stepping Off a Box
Bibliographic record
Abstract
It is common practice to study jump landing mechanics by having subjects step off a box set at a certain height instead of landing from a jump. This practice assumes that the landing mechanics are similar between stepping off a box and a countermovement jump as long as the heights can be matched. The mechanics of the two methods had never been compared when landing from identical heights. Thus, the purpose of this study was to compare the mechanics of landing from a countermovement jump to landing from a step-off. Participants performed three maximal countermovement jumps. The mechanics of one countermovement jump was compared with a center of mass fall height matched step-off landing. The step-off landing showed a more rapid time to peak ground reaction force (GRF) in both genders and greater GRF peak and loading rate in males only. No difference was observed between joint angles at initial contact; however, the countermovement jump showed significantly greater joint flexion angles at peak GRF for both genders. EMG showed greater muscle activity during the countermovement jump condition in all subjects. It was concluded that countermovement jump landings are different from step-off landings; thus, results from analyses involving step-off landings should be taken with caution if the aim is to relate them to landing from a 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".