A Comparison of Paddle Forces between Whitewater and Flatwater Training in C1 Canoe Slalom
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Becoming an elite canoe slalom athlete requires thousands of hours of training, spread over many years. It is difficult to assess the correct balance between flatwater and whitewater training because differences in the paddle forces on these terrains are not known. The aim of this study was to describe paddle forces during canoe slalom training on flatwater and whitewater courses for the C1 canoe category. METHODS: Paddle forces for twenty C1 canoe slalom athletes were quantified during all-out figure-of-eight tests on a flatwater course and during race simulations on a whitewater course. Paddle forces were measured using strain gauges embedded in the paddle shaft and quantified by their force, impulse, and stroke durations. RESULTS: The mean force during the pull phase of the paddle strokes was not significantly different between the flatwater and whitewater courses; however, the longer pull phase durations led to a greater pull phase impulse when paddling on the whitewater course. CONCLUSIONS: This study indicates that training for all-out runs on a whitewater course is more demanding for canoe slalom athletes than performing all-out trials on a flatwater figure-of-eight course. This evidence may help to develop effective training plans that are essential to reach the highest levels of the sport.
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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 | 0.001 |
| 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".