Canoe polo Athletes’ Anthropometric, Physical, Nutritional, and Functional Characteristics and Performance in a Rowing Task: Cross-Sectional Study
Bibliographic record
Abstract
Understanding the physical, functional, mental, and nutritional attributes of canoe polo athletes is essential for training and development. Forty-three canoe polo athletes (mean age: 21.54 ± 6.03) participated in the study and were assessed for: anthropometric measurements, exercise motivation, eating habits, adherence to the Mediterranean Diet, and physical and functional abilities. Correlation and multivariate analysis were conducted. Individual performance in a rowing task showed body mass index (β = 0.41) and female gender (β = 0.34) to be the strongest anthropometric predictors, whereas body fat (β = −0.35) and triceps brachii skinfold fatty tissue (β = −0.35) were the strongest negative predictors. Pushing strength (β = 0.37) and range of motion with internal rotation (β = 0.30) were the strongest physical predictors. The physical dimension of the Exercise Motivation Index was a significant psychosocial predictor (β = 0.27). Senior participants had a higher waist–hip ratio (p = 0.04, d = 0.66), arm circumference (p = 0.03, d = 0.68), handgrip strength (p < 0.01, d = 1.27), and push strength (p < 0.01, d = 1.42) than under 21-year-olds. Understanding the highlighted sport-specific characteristics of canoe polo athletes can help trainers to design programs at all levels to optimize performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".