Bibliographic record
Abstract
BACKGROUND.Most participants in long-distance cross-country ski races (loppets) are masters (age 30 yrs).They represent an effective study population to quantify the age-performance relationship.AIM. 1) to determine the relationship between age, gender, skiing style and performance of master skiers; 2) to test the force development theory that suggests that the decreased performance with age should be larger for the more strenuous free style technique than for the classic style, for both men and women.MEASURES.Cross-sectional data were gathered from nine loppets from the Worldloppet Circuit that comprised 89 events in total between 1995 and 2005.A total of 190,304 master men and 24,917 master women took part to these events.Participant age was classified mainly in 5-year categories while average speed was calculated from the loppet distance divided by individual race times.MODELING APPROACH.The boundary line approach was used to select the maximum performance by age class in each event.ANALYSES.A general modified power model was fitted to the relative average maximum speed achieved in each age category for each event.Loppets were considered as random factor and each event within loppets as subjects in a repeated-measure regression analysis.OUTCOMES.Age, gender and skiing style influenced maximum performance.The general model fit was considered good (r 2 = 0.70; p < 0.001).Maximum performance of skiers decreased with age, with more moderate declines for men than for women, and also for the classic style compared with the free style.Age correction factors were calculated direct from the general model, giving preliminary age-graded factors making comparable performances at different ages in various loppet events.CONCLUSIONS.The results supported the force development theory.However, the study of longitudinal data is needed to confirm or refine age-graded factors, particularly for elderly skiers ( 60 yrs).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".