Reducing gravity takes the bounce out of running
Bibliographic record
Abstract
Abstract In gravity below Earth normal, a person should be able to take higher leaps in running. We asked ten subjects to run on a treadmill in five levels of simulated reduced gravity and optically tracked center of mass kinematics. Subjects consistently reduced ballistic height compared to running in normal gravity. We explain this trend by considering the vertical takeoff velocity (defined as maximum vertical velocity). Energetically optimal gaits should balance energetic costs of ground-contact collisions (favouring lower takeoff velocity), and step frequency penalties such as leg swing work (favouring higher takeoff velocity, but less so in reduced gravity). Measured vertical takeoff velocity scaled with the square root of gravitational acceleration, following energetic optimality predictions and explaining why ballistic height decreases in lower gravity. The success of work-based costs in predicting this behaviour challenges the notion that gait adaptation in reduced gravity results from an unloading of the stance phase. Only the relationship between takeoff velocity and swing cost changes in reduced gravity; the energetic cost of the down-to-up transition for a given vertical takeoff velocity does not change with gravity. Because lower gravity allows an elongated swing phase for a given takeoff velocity, the motor control system can relax the vertical momentum change in the stance phase, so reducing ballistic height, without great energetic penalty to leg swing work. While it may seem counterintuitive, using less “bouncy” gaits in reduced gravity is a strategy to reduce energetic costs, to which humans seem extremely sensitive. Summary Statement During running, humans take higher leaps in normal gravity than in reduced gravity, in order to optimally balance the competing costs of stance and leg-swing work.
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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 | 0.001 | 0.000 |
| 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".