Influence of slope and speed on spatio-temporal variability of recreational runners
Bibliographic record
Abstract
Variability of spatio-temporal running parameters (STp) provides insights into the runner's self-organisation to the running environment. The aim of this study was to examine the influences of speed and slope on linear and non-linear measurements of variability for various STp (i.e. contact, flight, step times, step length, duty factor, leg and vertical stiffness). Twenty recreational runners (age 24.8 ± 6.3 years; height 1.8 ± 0.1 m; weight 64.0 ± 9.8 kg) completed 10-min randomized bouts of treadmill running at different conditions (80-120% of preferred running speed by 10% increments, ±2, 5 and 8% of slope). The influences of speed on the linear measurements of variability varied depending on the STp. As speed increased, there was no change in the linear measurement of step time variability, but a decrease for contact time variability. Negative slopes exhibited higher linear measurements of STp variability compared to flat or positive slopes, with results like those seen in slower speed conditions. Neither slope nor speed impacted non-linear measurements of STp variability, likely due to the use of moderate slope gradients and preferred running speed derived conditions. Future research should explore a wider range of conditions to gain a more comprehensive understanding of slope influence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".