An observational model for biomechanical assessment of sprint kayaking technique
Bibliographic record
Abstract
Sprint kayaking stroke phase descriptions for biomechanical analysis of technique vary among kayaking literature, with inconsistencies not conducive for the advancement of biomechanics applied service or research. We aimed to provide a consistent basis for the categorisation and analysis of sprint kayak technique by proposing a clear observational model. Electronic databases were searched using key words kayak, sprint, technique, and biomechanics, with 20 sources reviewed. Nine phase-defining positions were identified within the kayak literature and were divided into three distinct types based on how positions were defined: water-contact-defined positions, paddle-shaft-defined positions, and body-defined positions. Videos of elite paddlers from multiple camera views were reviewed to determine the visibility of positions used to define phases. The water-contact-defined positions of catch, immersion, extraction, and release were visible from multiple camera views, therefore were suitable for practical use by coaches and researchers. Using these positions, phases and sub-phases were created for a new observational model. We recommend that kayaking data should be reported using single strokes and described using two phases: water and aerial. For more detailed analysis without disrupting the basic two-phase model, a four-sub-phase model consisting of entry, pull, exit, and aerial sub-phases should be used.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".