Including visual criteria into predictive simulation of acrobatics to enhance the realism of optimal techniques
Bibliographic record
Abstract
To perform their acrobatics successfully, trampolinists make real-time corrections mainly based on visual feedback.Despite athletes' heavy reliance on visual cues, visual criteria have not been introduced into predictive simulations yet.We aimed to introduce visual criteria into predictive simulations of the backward somersault with a twist and the double backward somersault with two twists in pike position to generate innovative and safe optimal acrobatic techniques.Different visual vs kinematics objective weightings were tested to find a good compromise.Four international coaches and two international judges assessed animations of the optimal techniques and of an elite athletes technique, providing insights into the acceptability of the optimal techniques.For the most complex acrobatics, coaches found the optimal techniques more efficient for aerial twist creation.However, they perceived them as less safe, less realistic, similarly aesthetic, and similarly appropriate for visual information intake compared with the athlete's technique.The scores given by the judges were twice as high for the optimal technique compared to the athlete's technique.This study highlights the importance of including visual criteria into the optimization of acrobatics to improve the relevance of the optimal techniques for the sporting community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".