The Effect of Aerobic Capacity on Maintaining Maximum Skating Speed on Ice
Bibliographic record
Abstract
PURPOSE: To measure the effect of maximal aerobic capacity on maintaining maximum mid-duration (45-50 sec) skating speed during a 12 time repeat 20 meter stop and go shuttle skate. METHODS: Maximal aerobic skating capacity was measured on-ice as described by Leone et al and adapted by Whittom et al for direct measurement using a portable metabolic cart (K4B2, Cosmed, It). Briefly, professional hockey players (n=132) skated over a 45 meter distance at a rhythm given by an auditory signal beginning at the goal line, midway (redline) and at the end (opposite goal line) of the ice surface for one minute (stop and go at the goal lines). There was a 30 second rest period at the end of each minute before beginning the next stage, as described above, but now at a quicker pace set by the auditory signals. The player would continue this procedure until reaching maximal skating speed. The test would stop when the players could no longer keep pace with the auditory signals. This stopping point was defined as O2max. Following two days of rest, the players were then instructed to perform an all out on ice wingate defined as skating back and forth (stop and go) as fast as possible over 20 meters (roughly blue line to blue line) 12 times. Time for each back and forth sequence (total of 6) was clocked by two independent observers with time watches to the 100th of a second. RESULTS: On ice wingate time (s) per lapCONCLUSION: Players with VO2max values above 58 ml/kg/min were quicker at every lap when compared to players with lower VO2max values except for lap 1 where no significant differences between player lap times were observed. Indicating that short durations (<7s) are dependant on anaerobic power while sustained anaerobic power (<50s) is partially dependent on aerobic capacity.
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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) | 0.002 | 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".