Sport Equipment Evaluation and Optimization - A Review of the Relationship between Sport Science Research and Engineering
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
In current sport equipment evaluation and optimization, most studies consider the body and an equipment together as one system. This is partially because equipment optimization is mainly done through modification of mechanical designs, thus equipment evaluation is conducted through statistical comparisons of how different mechanical designs perform under human usage. However, it is known that any change in the performance environment would cause one to adapt certain aspects of his or her movements. Variation in equipment is considered as such a performance-altering environmental change. Yet, this equipment-induced motor control change is hardly studied in sport equipment evaluation/optimization, such as studies on golf clubs, pole-vaulting poles and hockey sticks. Without a thorough understanding of the interactions between equipment alteration and human motor control adaptation, equipment optimization is like a hit-and-miss game. Therefore this paper aims: 1) to look back at the different generations (eras) in the development of sports equipment, 2) to elaborate the roles of engineering and sport science/motion analysis technology in each generation and 3) to discuss the essence of sport science research in sport equipment optimization, which has evolved beyond pure engineering. One focus of this review is on body-equipment interactions and body movement adjustments in response to different equipment designs. Both these aspects should ideally be included in future studies related to sports equipments.
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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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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