Traction of clogged golf footwear
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it