Development of Anaerobic Fitness in Top-Level Competitive Youth Ice Hockey Players
Bibliographic record
Abstract
Leiter, JR, Cordingley, DM, and MacDonald, PB. Development of anaerobic fitness in top-level competitive youth ice hockey players. J Strength Cond Res 32(9): 2612-2615, 2018-Ice hockey is a physiologically complex sport involving both the anaerobic and aerobic energy systems. The purpose of this study was to evaluate the anaerobic power output (PO) of top-level competitive youth hockey players. It was hypothesized that with each successive increase in age, there would be an associated change in anaerobic PO. Two hundred and fifty-one male hockey players between the ages of 13-17 years participated in this study. All athletes completed a 30-second Wingate test as part of a preseason physiological and fitness combine. A 1-way analysis of variance was performed to compare peak PO (POpeak), average PO (POavg), and fatigue index between all age groups. A Tukey's post hoc test was used to determine changes in immediately successive age groups for all variables. Age categories were grouped as 13 years old (yrs) (n = 72), 14 yrs (n = 68), 15 yrs (57) and 16 yrs (n = 54, including 11 athletes 17 yrs). Absolute POpeak significantly increased with all age increases. Relative POpeak, absolute POavg, and relative POavg increased between the ages of 13 and 14 years, and 14 and 15 years, but not between the ages 15 and 16 years. There were no changes in fatigue index between any successive age groups. Anaerobic PO increases with an increase in age with no associated change in fatigue index. Athletes, coaches, and parents can use this normative data to help prepare the player for upcoming seasons in which there may be an increase in level or age class.
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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 | 0.001 | 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) | 0.001 | 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".