Traction of clogged golf footwear
Bibliographic record
Abstract
Purpose: In the game of golf, players are constantly moving into and out of many different terrains as well as playing on different ground conditions, which may cause the shoe-surface interface to become contaminated with solid debris. The addition of this debris will no doubt alter the shoe-surface interface, potentially altering the golfer's traction and performance during the swing. Therefore, the purpose of this study was to compare the traction of two different types of golf cleats on wet and dry surfaces, while the traction elements were both clean and clogged with debris. Methods: The traction of footwear with two different cleat combinations on wet and dry natural grass, while the traction elements were unclogged and clogged with debris was tested. A robotic testing machine that encompassed six degrees of freedom was used for all traction testing. A normal load of 500 N was applied to the shoe, after which the platform moved at a speed of 75 mm/s, with the horizontal and vertical forces being measured by the load cell during the duration of the movement. Results: Clogging of the cleats significantly reduced traction (F = 63.823, p < 0.001) as did wetting the surface (F = 9.964, p = 0.002). Testing location of the shoe caused a difference in traction measurements with the forefoot having significantly higher traction values than the rearfoot (F = 49.617, p < 0.001). Cleat type did not have a significant effect on traction (F = 1.364, p = 0.247). Conclusion: If footwear is clogged with course contaminants, significant reductions in traction could occur, which may lead to slipping and result in altering the timing or motion of the swing as well as the ability to transfer the required force through the body to the ball.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".