Bibliographic record
Abstract
Abstract Physical conditioning, technical ability, contact, and protective equipment have been identified through research as factors that can potentially contribute to the incidence of injuries in ice hockey players. One safety-related factor often overlooked is the interaction between the skate blade and the ice. Skating is one of the fundamental skills of a successful hockey player, but the effect of skate sharpening on blade characteristics and performance has received limited research attention. The point of contact with the ice is essentially what allows the transition of human motion to skating mechanics, and it may affect both the quality of skating performance and the potential for on-ice injuries. The purpose of this paper is to address the influence of skate blade sharpening characteristics on performance. Experiments performed to examine skate blade sharpening characteristics have identified radius of hollow (ROH), radius of contour (ROC), pitch and levelness of edges as variables that can be manipulated, quantified, and controlled when analyzing blade-ice interaction and the effect of skate sharpening on skating performance. Optimum values for each may produce more effective skating performances. Less than optimum values can result in slower speeds, longer stopping times, instability, body malalignment, greater fatigue, and potentially, greater chance of injury. Being able to define blade characteristics and determine the best combination of ROH, ROC, and pitch for a specific player allows some degree of control in an environment which can often be unpredictable. Furthermore, although there are standards for acceptable ice in professional hockey leagues, very often players must skate on a surface which is not only less than ideal, but which can also change over the course of a game or practice. Careful sharpening to accommodate for less than ideal ice conditions and the unpredictable nature of the play may also help to prevent fatigue, and injuries.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".