Gender difference in cycling speed and age of winning performers in ultra-cycling – the 508-mile “Furnace Creek” from 1983 to 2012
Bibliographic record
Abstract
We analysed (i) the gender difference in cycling speed and (ii) the age of winning performers in the 508-mile "Furnace Creek 508". Changes in cycling speeds and gender differences from 1983 to 2012 were analysed using linear, non-linear and hierarchical multi-level regression analyses for the annual three fastest women and men. Cycling speed increased non-linearly in men from 14.6 (s = 0.3) km · h(-1) (1983) to 27.1 (s = 0.7) km · h(-1) (2012) and non-linearly in women from 11.0 (s = 0.3) km · h(-1) (1984) to 24.2 (s = 0.2) km · h(-1) (2012). The gender difference in cycling speed decreased linearly from 26.2 (s = 0.5)% (1984) to 10.7 (s = 1.9)% (2012). The age of winning performers increased from 26 (s = 2) years (1984) to 43 (s = 11) years (2012) in women and from 33 (s = 6) years (1983) to 50 (s = 5) years (2012) in men. To summarise, these results suggest that (i) women will be able to narrow the gender gap in cycling speed in the near future in an ultra-endurance cycling race such as the "Furnace Creek 508" due to the linear decrease in gender difference and (ii) the maturity of these athletes has changed during the last three decades where winning performers become older and faster across years.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".