Characteristics of Lower Extremity Work During the Impact Phase of Jumping and Weightlifting
Bibliographic record
Abstract
Jumping and weightlifting tasks involve impact phases, where work is performed by the lower extremity to absorb energies present at contact. This study compared the lower extremity kinematic and kinetic strategies to absorb energy during the impact phase of jumping and weightlifting activities. Ten women experienced in jumping and weightlifting performed 4 tasks (landing from a jump, drop landing, clean, and power clean) in a motion analysis laboratory. Work performed at the hip, knee, and ankle were calculated during the landing and receiving phases of jumping and weightlifting tasks, respectively. Additionally, segment and joint kinematics and net joint moments were determined. The most lower extremity work was performed in the clean and drop landing, followed by landing from a jump, and the least work was performed in the power clean (p < 0.05). For all tasks, work performed by the knee extensors was the greatest contributor to lower extremity work. Knee extensor net joint moment was greater in the power clean than jump and drop landings, and greater in the clean than all other tasks (p < 0.05). Knee flexion angle was not different between the power clean and jump landing (p > 0.05) but greater in the drop landing and clean (p < 0.05). A common characteristic of the impact phase of jumping and weightlifting tasks is a large contribution of knee extensor work. Further, the correspondence in kinematics between impact phases of jumping and weightlifting tasks suggests that similar muscular strategies are used to perform both types of activities. Weightlifting tasks, particularly the clean, may be important exercises to develop the muscular strength required for impact actions due to their large knee extensor net joint moments.
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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) | 0.002 | 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".