Velocity distribution of women's 30-km cross-country skiing during Olympic Games from 2002-2014
Bibliographic record
Abstract
BACKGROUND: Within several investigated endurance sport disciplines the distribution of load of the best competitors has a manner of evenly or slightly rising velocity values. Unfortunately many other competitors have usually diminishing values or when they are very poor they have evenly values. The aim of this study was to investigate distribution of velocity within 30-km cross-country female skiers. METHODS: Cross-country skiing runs were investigated of Olympic Games 2002-2014 (Salt Lake City, Turin, Vancouver, Sochi). At every race two 15 km or three 10 km loops of the same vertical profile were taken into account. The competitors were divided onto: A) winners; B) medalists; C) competitors who obtained places 4 to 10 at the finish line (medium runners); and D) competitors who obtained places 11 to 30 at the finish line (poor runners). Velocity data presented on the web pages of several institutions were utilized. RESULTS: The competitors had their velocity distributed in a manner with usually diminishing values. While comparing velocity of sequential loops with the mean velocity the difference for the poor runners reached the value of almost 6%, which was too high. There was significant (usually negative) correlation coefficient between values of velocity deviation for the first and second loops and the mean value of velocity for the entire distance for the better runners and mixed, i.e. positive and negative values for the poorer runners. CONCLUSIONS: It was postulated investigations of velocity distribution should be introduced in coaching in order to inform competitors about their running. This advises is especially important for the poorer runners. Up to now cross-country skiers run for themselves. It should be discussed whether the tactics used by road and track runners, i.e. running with pace makers, can be introduced in cross country skiing. Also the use of a drone during training can be used in order to maintain proper pace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".