Best of the best: A cohort study of race performance characteristics of eminent endurance cyclists
Bibliographic record
Abstract
The characteristics of those who advance the frontiers of human capabilities have fascinated scientists for centuries. Studying those who operate at, and advance, the edges of human performance may hold lessons that could benefit athlete development for all ages and stages of competition. To help contribute to this research area, the present study explored race performance characteristics of the world's best endurance cyclists. All race results listed on the Union Cyclisme International database for athletes between the years 2010–2024 (N = 5,168,668 total race observations; N = 107,024 unique athletes [21.12% women and 78.88% men]) were considered. Basic descriptive statistics (e.g., gender, location, confederation, and discipline) were used to describe ‘eminent’ athletes. This eminent sample was further explored relative to their discipline status (i.e., those who race in one discipline, or multiple) and transition patterns. Findings indicate subtle differences between groups in terms of ages to milestones, duration between accomplishing milestones, and transition patterns.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".