A deterministic model based on evidence for the associations between kinematic variables and sprint kayak performance
Bibliographic record
Abstract
The aim of this narrative review was to propose a deterministic model based on a review of previous research documenting the evidence for the associations between average kayak velocity and kinematic variables in sprint kayaking. Literature was reviewed after searching electronic databases using key words 'kayak,' 'biomechanics,' 'velocity,' 'kinematics,' and 'performance.' Our kinematic deterministic model for sprint kayaking performance shows that the average kayak velocity is determined by kayak stroke displacement and stroke time. Stroke time had the strongest correlation with 200-m race time (r = 0.86, p < 0.001), and stroke rate (inversely proportional to stroke time) was strongly correlated with average horizontal velocity over two consecutive strokes at race pace (r = -0.83, p < 0.05). Increased stroke rate via decreased absolute water phase time and increased relative water phase time were indicative of more elite performance. There was no significant relationship between stroke displacement and velocity; however, a large decrease in stroke displacement may be detrimental to performance. Individual characteristics may be responsible for a paddlers' ability to achieve and sustain a given stroke rate. Coaches should theoretically focus interventions on increasing stroke rate while maintaining stroke displacement; however this hypothesis should be confirmed with prospective studies.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".