In search of the 70 kph human: challenging the limits of human muscle contraction time, a pilot investigation
Bibliographic record
Abstract
Interest in sprint running has been fueled by the remarkable performance in 100-and 200-metre events at the 2008 Olympic Games. Amid this interest, speculation mounts as to how fast humans can run and to the existence of new types of fast-twitch fibers as the mechanism that realizes faster per formances. This paper adopts the view that humans are limited in how fast they can run by how much force they can apply within the muscle contraction times inherent of fast running and proposes a method by which adaptation may be forthcoming to strengthen the locomotive muscles in humans within that required contraction time or shorter contraction times. The proposed method consists of the fast foot drill exercise executed with the intent of increasing the rate of stepping; training with this method was carried out over 16 weeks. The analysis of the post-training stepping rate shows that movement frequencies in human locomotive muscle approaches 7 muscle contractions per second. The analysis also shows that muscle activation times approach 90 milliseconds for the vastus lateralis muscle and 55 milliseconds for the biceps femoris muscle. Furthermore it is speculated that as a result of this training method muscle contraction times may approach and surpass the time limits for humans that are currently accepted in science. The hypothesis is that by combining the fast foot drill with progressive external resistance, runners can increase their force production within the ground contact time inherent of fast running that currently limits how fast humans can run.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".