Influence of Artificial Turf Surface Stiffness on Athlete Performance
Bibliographic record
Abstract
Properties of conventional playing surfaces have been investigated for many years and the stiffness of the surface has potential to influence athletic performance. However, despite the proliferation of different infilled artificial turfs with varying properties, the effect of surface stiffness of these types of surfaces on athlete performance remains unknown. Therefore, the purpose of this project was to determine the influence of surface stiffness of artificial turf systems on athlete performance. Seventeen male athletes performed four movements (running, 5-10-5 agility, vertical jumping and sprinting) on five surfaces of varying stiffness: Softest (-50%), Softer (-34%), Soft (-16%), Control, Stiff (+17%). Performance metrics (running economy, jump height, sprint/agility time) and kinematic data were recorded during each movement and participants performed a subjective evaluation of the surface. When compared to the Control surface, performance was significantly improved during running (Softer, Soft), the agility drill (Softest) and vertical jumping (Soft). Subjectively, participants could not discern between any of the softer surfaces in terms of surface cushioning, however, the stiffer surface was rated as harder and less comfortable. Overall, changes in surface stiffness altered athletic performance and, to a lesser extent, subjective assessments of performance, with changes in performance being surface and movement specific.
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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) | 0.002 | 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".