Stroke technique in C1 canoe slalom: a simulation study
Bibliographic record
Abstract
Male C1 canoe slalom athletes traditionally used cross transitions to move their paddle to the other side of the boat and off-side strokes to paddle on their non-dominant side. Conversely, female athletes often use a switching transition and on-side strokes on their non-dominant side. The purpose of this study was to use a computer model to assess the relation between cross- or switching techniques, and the relative strength (symmetry) of non-dominant compared to dominant side strokes to race times in C1 canoe slalom. We created a forward dynamics model to predict race times using stroke forces (from an indoor ergometer), drag forces (measured on-water), and probability distributions for stroke and transition times (measured from international canoe slalom competitions). The main effects from an ANOVA (p<0.05) were (i) for a given transition number and strength symmetry the race times were faster when using cross-transitions than switch-transitions (ii) for a given strength symmetry the race times became slower as the number of switch transitions increased, but there was minimal effect of the number of cross-transitions, and (iii) the closer the strength of the strokes were between the dominant and non-dominant side (as symmetry factor approached 100 %), the faster the race times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".