Upper body muscle activity during C1 canoe slalom strokes
Bibliographic record
Abstract
Canoe slalom is a white-water sport involving a single bladed paddle that must be used on both sides of the boat, which creates two stroke types.On-side strokes have the bottom hand on the same side as the paddle blade, with the top hand and arm crossing the midline of the body.In contrast, off-side strokes have the top hand on the same side as the paddle blade, with the bottom hand and arm crossing over the midline.The purpose of this study was to compare the use of upper body and arm muscles during on-side and off-side strokes in slalom C1 canoeing.Surface electromyography (EMG) was recorded bilaterally from the biceps (BI), triceps (TRI), latissimus dorsi (LAT), and erector spinae (ES) muscles during an ergometer test for two C1 athletes and bilaterally from the BI and TRI of one athlete on a flatwater figure-ofeight course.The normalized Root-Mean-Square (RMS) EMG was calculated and used for statistical analysis.Stroke type had a significant effect on the EMG for the BI, TRI, and LAT muscles, but there was no such effect on the ES.Off-side strokes had a notable increase in activation for the BI for the bottom-hand muscles, and the TRI for the top-hand muscles.The current results support the understanding that activation of muscles in the upper-arm, and presumably arm strength, is highly important for off-side strokes, and this may contribute to differences in the selection of paddle strokes by different athletes.
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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 | 0.001 | 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".