Pacing strategies and relationships between speed and stroke parameters for elite sprint kayakers in single boats
Bibliographic record
Abstract
Joshua A. Goreham , Kayla Bugeya Miller , Ryan J. Frayne & Michel Ladouceur* School of Health and Human Performance, Dalhousie University, Halifax, Nova Scotia, CanadaCONTACT Michel Ladouceur josh.goreham@dal.ca Dalhousie University 6260 South Street, Halifax B3H 4R2, Nova Scotia, CanadaABSTRACTThe study aimed to determine the pacing strategies of elite single-boat sprint kayakers, as well as the relationships between stroke parameters (stroke rate (SR) and stroke length (SL)) and kayak speed throughout the race. High-resolution split speed and stroke parameter data from men’s (MK1) and women’s (WK1) single-boat A- and B-finals in 2016–2017 international sprint kayak competitions were analysed. Correlation coefficients were calculated between SR-speed and SL-speed during each split for each race group. Athletes followed all-out, positive and seahorse-shaped pacing strategies for the 200 m, 500 m and 1000 m races, respectively. SL-speed had greater correlations during the first half of the MK1 200 m race, whereas SR-speed had greater correlations during the second half. SR-speed correlations were greater than SL-speed correlations throughout the final 150 m of WK1 200 m races. There were large and very large correlations between SR-speed at the end of both the WK1 500 m and MK1 1000 m race distances, respectively, despite following different pacing strategies. Single-boat pacing strategies change due to race distance during major international sprint kayak competitions, whereas the relationships between stroke parameters and speed change depending on athlete sex and the race distance.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".