Bibliographic record
Abstract
Gear ratios in locomotion can be defined as the ratio of lever arms between the ground reaction force (GRF) and the calf muscle to the ankle joint. A resultant change in torque can be developed by either lengthening the distance of the GRF arm, or by altering the direction of the GRF. Studies on sports equipment such as speed skating and bicycling have shown that altering the gear ratios can affect performance. The purpose of this study was to investigate the effects of gear ratios on ankle joint moment and power generation. Seven male subjects participated in the study (US size 9). Each subject performed sprints out of blocks in three conditions: a control, and two modified shoe conditions with carbon plate insoles inserted, one size 9 and one of size 11 adjusted to fit a size 9 runner. Kinematic and kinetic data were collected from the first stride out of the blocks and used to calculate ankle angle, torque, velocity and power. In a separate session, isometric and isokinetic measurements of the ankle plantarflexor strength were made in order to establish the torque-angle and torque-velocity relationships for each individual. Results showed that stiffening the sprint spike increased the gear ratio about the ankle. A larger torque was generated, mainly due to the shift in ankle range of motion toward a more dorsiflexed position. This can be supported by the torque-angle relationship determined from isometric strength measurements. Angular velocity decreased, offsetting the torque increase. As the result, no significant changes in power and energy produced were observed. Looking at individual results, two subjects showed a large increase in torque generation without decreasing angular velocity resulting in increased power, which is valued as increased sprinting performance, with a larger gearing ratio.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".