Bibliographic record
Abstract
Abstract One of the most fundamental skills on the pommel horse is double leg circles (circles). Circles can be performed on all parts of the pommel horse. It was hypothesized that the different sets of physical constraints of the apparatus require a gymnast to adapt circles technique. The purpose of this study was to quantify how gymnasts modify their technique during circles to accommodate different physical obstacles due to different support surfaces and body orientations. To investigate these adaptations, a comparison of the two most common circles on pommel horse – namely, circles in cross support (cross‐circles) and circles in side support (side‐circles) – was carried out. Seven elite male gymnasts performed three sets of 10 cross‐circles and side‐circles on a pommel horse. Three‐dimensional coordinates were acquired using 12 Qualisys ProReflex motion tracking cameras operating at 120 Hz. Temporal characteristics, the motion of the centre of mass, and the body angles – flexion and lateral flexion – were analysed. We found that cross‐circles took less time to complete a single circle (0.92±0.02 vs. 0.95±0.02 s), had a smaller ratio for the rear support phase (0.15±0.02 vs. 0.18±0.03), a narrower supporting‐hand distance (0.33±0.03 vs. 0.52±0.00 m), greater flexion of the body over the rear support phase (44±12° vs. 27±8°), and less lateral flexion of the body over the single‐hand support phase (entry: 20±5° vs. 35±3°; exit: 26±4° vs. 33±4°) than side‐circles. Our results suggest that gymnasts adapt their technique to the physical constraints imposed by the shape of the pommel horse and the location and orientation of the circles. Cross‐circles were characterized by a high rear support position with a narrower hand‐spacing, which would require excellent shoulder flexibility and strength. Consequently, lack of these traits may explain the need for greater body flexion in cross‐circles than in side‐circles during rear support. Understanding the technical differences will facilitate an effective transfer of the technical similarities among different types of circles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".