Potential Method of Optimizing the Klapskate Hinge Position in Speed Skating
Bibliographic record
Abstract
Acceptance of the klap speed skate was fully realized on the world speed skating scene in 1997. However, one of the most important unknowns regarding the klapskate was the positioning of the point of foot rotation (pivot point), which is believed to play an important role in optimizing klapskate performance. The purposes of this study were to explore the ankle, knee, and hip joint mechanical changes that occurred when the pivot point location was modified, and to determine whether maximal ankle torques provide predictive ability as to where the optimal pivot point positioning is for a skater. We tested 16 proficient skaters at three pivot point PP) locations, ranging from just in front of the metatarsal-phalangeal joint to just in front of the first phalangeal joint. Of the 16 skaters, 10 were tested at a fourth position; tip of the toe. Push phase kinetics and kinematics were measured on a modified slide board. The optimal PP for each skater was defined as the position that allowed him to generate the most total push energy. Maximum voluntary static torque measures of the ankle and knee were collected on a Biodex dynamometer. Overall, anterior pivot point shifting led to a significant increase in ankle energy generated and a decrease in knee energy generated, with no significant change at the hip joint. We found no significant correlations between the static strength measures and the skaters' optimal pivot points.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".