Ice friction of flared ice hockey skate blades
Bibliographic record
Abstract
In ice hockey, skating performance depends on the skill and physical conditioning of the players and on the characteristics of their equipment. CT Edge have recently designed a new skate blade that angles outward near the bottom of the blade. The objective of this study was to compare the frictional characteristics of three CT Edge blades (with blade angles of 4 degrees, 60, and 8 degrees, respectively) with the frictional characteristics of a standard skate blade. The friction coefficients of the blades were determined by measuring the deceleration of an aluminium test sled equipped with three test blades. The measurements were conducted with an initial sled speed of 1.8 m s(-1) and with a load of 53 kg on each blade. The friction coefficient of the standard blades was 0.0071 (s = 0.0005). For the CT Edge blades with blade angles of 4 degrees, 6 degrees, and 8 degrees, friction coefficients were lower by about 13%, 21%, and 22%, respectively. Furthermore, the friction coefficients decreased with increasing load. The results of this study show that widely accepted paradigms such as "thinner blades cause less friction" need to be revisited. New blade designs might also be able to reduce friction in speed skating, figure skating, bobsledding, and luge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".