Stance affects balance in surfers
Bibliographic record
Abstract
Surfing is a dynamic sport and is performed in a highly unstable and changing environment, making balance a vital characteristic for surfers. It might be expected that repeated practice of particular movements in a specific stance, such as surfing, would lead to specific balance adaptations. This study investigated dynamic balance within surfers while also evaluating the influence of stance. Balance was assessed using the Biodex Stability System in an upright bipedal stance in 20 adult male surfers (age 24.10 ± 2.40 years, mass 74.95 ± 8.26 kg, height 177.11 ± 6.13 cm). Three 20-second balance trials were performed, and the degree and direction of tilt from horizontal were recorded. Results indicated that regular stance surfers spent a significantly ( p < 0.05) greater percentage of time (66.18% ± 26.28) in horizontal balance compared to goofy stance surfers (44.03% ± 18.96). Regular stance surfers spent a significantly greater percentage of time (38.81% ± 14.77) in a posterior direction compared to goofy stance surfers (20.09% ± 7.25), while goofy stance surfers spent a significantly greater percentage of time in an anterior direction (40.50% ± 21.61) compared to regular stance surfers (16.04% ± 8.84). Surf stance appears to play a large role in horizontal and directional balance. These findings suggest the repetition of particular movements relative to stance may induce specific adaptations in surfers. Therefore, balance measurements may be used as an evaluation tool in this population.
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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 | 0.000 | 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) | 0.003 | 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".