Methodological Guidelines Designed to Improve the Quality of Research on Cross-Country Skiing
Bibliographic record
Abstract
Abstract Cross-country (XC) ski races involve a variety of formats, two different techniques and tracks with highly variable topography and environmental conditions. In addition, XC skiing is a major component of both Nordic combined and biathlon competitions. Research in this area, both in the laboratory and field, encounters certain difficulties that may reduce the reliability and validity of the data obtained, as well as complicate comparisons between studies. Here, 13 international experts propose specific guidelines designed to enhance the quality of research and publications on XC skiing, as well as on the biathlon and Nordic combined skiing. We consider biomechanical (kinematic, kinetic and neuromuscular) and physiological methodology (at the systemic and/or muscle level), providing recommendations for standardization/control of the experimental setup. We describe the types of measuring equipment and technology that are most suitable in this context. Moreover, we also deal with certain aspects of nomenclature of the classical and skating sub-techniques. In addition to enhancing the quality of studies on XC skiing, Nordic combined and biathlon, our guidelines should also be of value for sport scientists and coaches in other disciplines where physiological and/or biomechanical measurements are performed in the laboratory and/or outdoors.
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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 | 0.601 | 0.699 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".