Editorial: Training and performance in canoe slalom
Bibliographic record
Abstract
Editorial on the Research Topic Training and performance in canoe slalomCanoe slalom presents significant scientific challenges due to its intricate nature.In this whitewater discipline, athletes navigate gates over a turbulent river course.The Olympic Games feature the K1 (kayak single) and C1 (canoe single) classes, along with the international challenge of the canoe double category (C2).In both C1 and C2 canoeing, athletes use a single-blade paddle, kneeling in the boat with their legs tucked beneath their body.Conversely, kayaking employs a double-bladed paddle with the athletes seated in the boat.Improving canoe slalom athlete performance requires interdisciplinary collaboration among biomechanists, physiologists, engineers, sports scientists, psychologists, and coaches, which was the focus of the Research Topic.The level of attention and resources for research in any sport can vary depending on factors such as popularity, funding, and institutional support.Sprint canoeing, an Olympic discipline practiced more widely than canoe slalom in many countries, receives greater attention from researchers, sports scientists, and funding agencies.This attention results in more research studies, methodologies, and advancements in knowledge compared to canoe slalom.For example, a search using the terms [(canoe slalom) AND (performance)] NOT (sprint) in the Web of Science database yielded 31 results.In contrast, using the terms [(sprint canoe) AND (performance)] NOT (slalom) returned over double the results, with 73 entries.This highlights the disparity in research attention between the two disciplines.Nonetheless, canoe slalom still attracts interest due to its unique challenges and potential for interdisciplinary studies focusing on athlete performance.In an effort to address this gap, Messias et al. systematically reviewed studies that evaluated and compared mechanical, physiological, and technical parameters with athlete performance in canoe slalom.Their review included eight eligible studies involving a total sample of 117 male athletes.The studies revealed significant associations between mechanical/physiological factors and slalom performance.Most studies highlighted the importance of high-force development and a significant role of aerobic metabolism during slalom tasks.Interestingly, although
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".