A Comparison of Ultra-Endurance Cyclists in a Qualifying Ultra-Cycling Race for Paris-Brest-Paris and Race across America—Swiss Cycling Marathon
Bibliographic record
Abstract
Ultra-endurance events test the adaptation of human physiology to extreme physical and mental demands, high levels of training, motivation, and physical conditioning among participants. To understand basic differences among participants according to the severity of the race, participants in qualifying events for two ultra-endurance cycling races, differing in length and intensity, were compared on measures of anthropometry, training, and support. One race was four times longer, required supporting teams, and racers typically had little sleep, which should lead to the qualifiers being substantially more highly trained than those from the shorter race. The qualifiers in the longer race had greater intensity in training while the qualifiers in the shorter race relied more on training volume. Different strategies and types of training reflected the different demands of the races. Future studies should evaluate personality and motivational differences in ultra-endurance events and between these athletes and athletes in other sports.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".