The Differences in Pacing Among Age Groups of Amateur Cross-Country Skiers Depend on Performance
Bibliographic record
Abstract
Abstract Pacing strategies have mainly been investigated for runners, but little is known for cross-country skiers. The aim of the present study was to examine the effects of performance and age on pacing strategies in cross-country skiing. All finishers (women, n = 19,375; men, n = 86,190) in the ‘Engadin Ski Marathon’ (42 km) between 1998 and 2016 were analysed for the percentage change of speed at 10 km (Change A), 20 km (Change B) and 35 km (Change C). They were classified in performance groups according to quartiles of average race speed (Q1, Q2, Q3 and Q4) and in 5-year age groups (<20, 20-24, 25-29… 85-89 years). Men were faster than women by +14.3% (15.2 ± 4.0 vs. 13.3 ± 3.3 km/h; p < 0.001, η2 = 0.215). A small impact of age group × performance group interaction on Change A was shown in women (p < 0.001, η2 = 0.026) and men (p < 0.001, η2 = 0.025), where Q1 augmented and Q4 attenuated the decrease in speed with aging. However, the impact of age group × performance group interaction on Change B and C was trivial (p = 0.002, η2 ≤ 0.010). Based on these findings, it was concluded that the differences in pacing among age groups depended on the performance level. Thus, the coaches and fitness trainers working with cross-country skiers should advise their athletes to consider both age and performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".