Skating Crossovers on a Motorized Flywheel
Bibliographic record
Abstract
Ice hockey requires frequent skater crossovers to execute turns. Our investigation aimed to determine the effectiveness of training crossovers on a motorized, polyethylene high-resistance flywheel. We hypothesized that high school hockey players training on the flywheel would perform as well as their peers training on ice. Participants were 23 male high-school hockey players (age 15-19 years). The study used an experimental prospective design to compare players who trained for 9 sessions on the 22-foot flywheel with players who trained for 9 sessions on a similarly sized on-ice circle. Both groups were compared with control subjects who were randomly selected from the same participant pool as those training on ice. All players were tested before and after their 3-week training regimens, and control subjects were asked to not practice crossovers between testing. Group 1 trained in a hockey training facility housing the flywheel, and group 2 trained in the ice hockey arena where testing occurred. Primary outcome measures tested in both directions were: (a) speed (time in seconds) required to skate crossovers for 3 laps of a marked face-off circle, (b) cadence of skating crossovers on the similarly sized circles, and (c) a repeat interval speed test, which measures anaerobic power. No significant changes were found between groups in on-ice testing before and after training. Among the group 1 players, 7 of 8 believed they benefited from flywheel training. Group 2 players, who trained on ice, did not improve performance significantly over group 1 players. Despite the fact that no significant on-ice changes in performance were observed in objective measures, players who trained on the flywheel subjectively reported that the flywheel is an effective cost-effective alternative to training on ice. This is a relevant finding when placed in context with limited availability of on-ice training.
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 | 0.001 |
| 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) | 0.003 | 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".