Training for success: Do simulated aerial landings replicate successful aerial landings performed in the ocean?
Bibliographic record
Abstract
PURPOSE: Physical preparation of competitive surfers includes substantial dry-land training. It is currently unknown, however, how closely these exercises replicate surfing maneuvers performed in the ocean. This study compared the technique features displayed by surfers when landing simulated aerial maneuvers on land to critical features previously established as necessary for surfers to successfully land aerials in the ocean during competition. METHODS: Fourteen competitive surfers (age 20.6 ± 5.7 years, height 178.1 ± 9.50 cm, mass 70.6 ± 10.8 kg) were recruited to perform two variations of a simulated aerial task, a Frontside Air (FA) and Frontside Air Reverse (FAR). Joint ranges of motion (ROM), center of pressure, and apparent gaze data were collected during the landing event. Paired t tests or Wilcoxon signed-rank tests were used to identify any significant differences in the outcome variables between the two aerial tasks. RESULTS: Participants displayed 100% and 60% of the critical features associated with successfully landing a FA and FAR, respectively. In both the simulated FA and FAR, participants landed in 1.0-3.7° of dorsiflexion, moving through significantly less ankle joint ROM in the lead limb during the FAR (P < .01). Participants also displayed significantly less knee and hip ROM (P = .002-.048) while landing the FAR compared to the FA. CONCLUSION: The simulated FA and FAR tasks are appropriate training tools for surfers to replicate most of the critical features that a surfer should display to successfully land aerial maneuvers in the ocean. These tasks therefore enable surfers to practice these complex movements in a controlled environment.
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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 | 0.003 |
| 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 | 0.001 |
| 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".