The effects of new Edea and Graf figure skating boots and used Graf boots on the kinetics and kinematics of landing after simulated on-ice jumps
Bibliographic record
Abstract
An increase in training intensity and the number of active participants and competitors in figure skating has been accompanied by an increasing frequency of injuries. The aim of this study was to investigate whether different brands of skates as well as the usage of the skates modify the kinetics and kinematics of the landing impact from a jump. New Graf Edmonton (NG), old used Graf Edmonton (OG) and new Edea Concerto (NE) skates were compared. Twelve participants completed six jump trials from 30 cm and 50 cm high boxes, respectively in all three skates and landed on a section of artificial ice placed on a laboratory floor. Landing kinematics (Oqus4 system, Qualisys, Sweden) and kinetics (force plate: Kistler, Switzerland; insoles: Pedar, Novel, Germany) were examined. Each participant acted as their own control for statistical comparison between the skates. The results confirmed that the kinetics and kinematics of the landing are affected by wearing different skates. During landing impacts in NG, participants had significantly greater dorsiflexion at initial contact (IC) and peak dorsiflexion of the ankle, peak flexion of the knee and also greater in-skate plantar forces (PF) than in NE, which may increase the risk of injury. In OG, participants had significantly greater peak flexion of knee and longer time from IC to first peak dorsiflexion (TP) of the ankle than in NG. The differences observed may be due to the different construction designs, such as height of the heel, used materials, and stiffness of the skates, which may affect injury occurrence.
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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) | 0.001 | 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".