Runners do not push off the ground but fall forwards via a gravitational torque
Bibliographic record
Abstract
The relationship between the affect and timing of the four forces involved in running (gravity, ground reaction force, muscle force, and potential strain energy) is presented. These forces only increase horizontal acceleration of the centre of mass during stance but not flight. The current hierarchical models of running are critiqued because they do not show gravity, a constant force, in affect during stance. A new gravitational model of running is developed, which shows gravity as the motive force. Gravity is shown to cause a torque as the runner's centre of mass moves forward of the support foot. Ground reaction force is not a motive force but operates according to Newton's third law; therefore, the ground can only propel a runner forward in combination with muscle activity. However, leg and hip extensor muscles have consistently proven to be silent during leg extension (mid-terminal stance). Instead, high muscle-tendon forces at terminal stance suggest elastic recoil regains most of the centre of mass's height. Therefore, the only external motive force from mid-terminal stance is gravity via a gravitational torque, which causes a horizontal displacement. The aim of this paper is to establish a definitive biomechanical technique (Pose method) that is easily taught to runners (Romanov, 2002): falling forwards via a gravitational torque while pulling the support foot rapidly from the ground using the hamstring muscles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".