Comparison of lower limb segment forces during running on artificial turf and natural grass
Bibliographic record
Abstract
INTRODUCTION: Artificial turf, soon after being introduced in the 1980s, became associated with an increased injury incidence in football players. While more recent generations of artificial turf have mitigated the problem, perception of the material is still widely negative. So, the decision to play the 2015 Fe'de'ration Internationale de Football Association Women s World Cup in Canada on artificial turf was met with vocal criticism by many players. One common approach is to assess injury incidence to quantify risk differences in playing surfaces. This, however, does not account for possible confounding variables or chronic injuries. Direct measurement of ground reaction forces is difficult because conventional multicamera-based motion capture and force plate equipment are limited in its use outside of dedicated laboratories. METHODS: We describe a method of generating realistic force data by using miniature load cells that are installed directly into the weight-bearing structure of the body. RESULTS: Pilot data show a significant (p<0.01) difference in peak forces on artificial turf (272% of body weight) and natural grass (229% of body weight). DISCUSSION: Invasive surgical procedures were avoided by installing the load cell into the prosthesis of an athlete with lower limb loss. As modern prosthetic devices allow a close approximation of able-bodied kinematics and kinetics, such prosthesis-based data are transferable to a general population.
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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 | 0.000 |
| 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".