Effects of implement and distance on the performance of a discrete motor skill
Bibliographic record
Abstract
Golf putting is an example of a discrete motor skill that needs to be developed to produce success in the game of golf. Golfers attempt to use various putter designs and practice in numerous different ways in order to generate a successful putting technique. The counterbalanced putter design has been developed in order to replace the long putter now banned by PGA rules and there is no conclusive knowledge of its effects on performance. The aim of this study is to identify kinematic variables that change when novices putt from various distances using a conventional and counterbalanced putter. 8 novices (minimal to no experience with golf) performed 75 trials at 3, 5, 7, 9, and 11 feet from a target per putter for a total of 150 trials. Means and standard deviations for backswing timing (BST), downswing timing (DST), backswing amplitude (BSA), downswing amplitude (DSA), and putter path (PP) were determined using Visualeyez Motion Analysis system and subsequent software as well as Matlab and other processing software. A 2 putter (conventional/counterbalanced) by 5 distance (3, 5, 7, 9, 11 ft) ANOVA with repeated measures at p Acknowledgments: Ran Zheng, Felix Ling
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".