Comparisons Between Off-ice Fitness And Body Composition Profiles Of Canadian Interuniversity Hockey Players
Bibliographic record
Abstract
PURPOSE: Strength and performance coaches often use overall body-composition measures to help assess, monitor, or predict an athlete’s fitness attributes or sport performance. Dual energy x-ray absorptiometry (DXA) evaluates full, and regional body-composition profiles (i.e. fat, lean and total tissue) of the arm, leg, android, and gynoid regions. The purpose was to determine if a regional body composition analysis provides greater insight into the off-ice fitness levels of elite Canadian interuniversity hockey players, compared to a full body-composition evaluation. METHODS: All 26-players completed a pre-season DXA scan and team fitness evaluation. Eight fitness tests were completed (i.e., t-test, flexibility, bench press 150lbs & 200lbs, 40 yard sprint, single-leg triple jump, grip, 300 meter shuttle) and were compared to the player’s whole-body, and regional body-composition using Pearson r correlations. RESULTS: Arm total mass was most correlated to bench press 150 and 200 respectively (r = 0.629, p < .01., r = 0.724, p < .01), as well as android lean tissue (r = 0.482, p < .05., r = 0.535, p < .01), and total full-body tissue (r = .414, p < .05., r = .500, p < .01). Greater lean leg tissue showed a positive association with single-leg triple jump distance (r = .461, p < .05), but no associations involving total or fat tissue were found with the test. Leg and gynoid fat percentages were the only regional measures correlated to longer 300m shuttle times (r = .590, p < .01). No composition measures were predictive of flexibility or the overall speed tests (i.e. T-Test, 40 yard sprint). CONCLUSIONS: While body composition profiles can be of interest to athletes and coaches, the regional ratio of fat-to-lean tissue does not appear to be a strong indicator of general fitness scores in university hockey players. Anthropometric measures and body-composition profiles help monitor athlete development, but should not be used to make generalizations regarding an athlete’s strength, power or anaerobic capabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".