Evaluation of the minimum energy hypothesis and other potential optimality criteria for human running
Bibliographic record
Abstract
A popular hypothesis for human running is that gait mechanics and muscular activity are optimized in order to minimize the cost of transport (CoT). Humans running at any particular speed appear to naturally select a stride length that maintains a low CoT when compared with other possible stride lengths. However, it is unknown if the nervous system prioritizes the CoT itself for minimization, or if some other quantity is minimized and a low CoT is a consequential effect. To address this question, we generated predictive computer simulations of running using an anatomically inspired musculoskeletal model and compared the results with data collected from human runners. Three simulations were generated by minimizing the CoT, the total muscle activation or the total muscle stress, respectively. While all the simulations qualitatively resembled real human running, minimizing activation predicted the most realistic joint angles and timing of muscular activity. While minimizing the CoT naturally predicted the lowest CoT, minimizing activation predicted a more realistic CoT in comparison with the experimental mean. The results suggest a potential control strategy centred on muscle activation for economical running.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".