Effect of jump heights, landing techniques, and participants on vertical ground reaction force and loading rate during landing on three different Korean teeterboards
Bibliographic record
Abstract
Korean teeterboard is one of the most physically and technically demanding circus disciplines. Two performers take turns jumping vertically and land with high impact. The aims of this study were to (1) compare the stiffness across three different teeterboards, and (2) compare Peak Landing Force (PLF) and Maximal Loading Rate (MLR) of four acrobats performing jumps from three teeterboards using four landing techniques (normal, smooth, straight legs, and empty board). Pressure sensors were used to determine recorded forces under the feet, while Boosted Regression Trees (BRT) was used to analyze factors contributing to PLF and MLR. Standard static loading protocol was used to estimate teeterboard stiffness. PLF and MLR increased with jump height. PLF and MLR were reached when landing on the teeterboard with the highest stiffness. The “normal” and “straight legs” landing techniques were associated with higher PLF and MLR. The BRT model was able to associate both PLF and MLR with jump height, participant, teeterboard, and landing technique factors. PLF reached 13.5 times the body weight when landing on the stiffer teeterboard using the straight legs technique. Trainers should be aware of the injury risk to teeterboard acrobats during landing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".