Sex differences in ultramarathon performance in races with comparable numbers of males and females
Bibliographic record
Abstract
There is a prominent sex-based difference in athletic performance such that males outperform females by 7%–14% in races from 100 m to marathon. In ultramarathons, the difference is often much smaller, leading to speculation that females are “built” for the sport. However, data are confounded by the low number of female participants; just 10%–30% in any given race. This study compared data from two ultramarathons where males and females competed in comparable numbers. There were 116 and 146 starters in the 50 mile and 100 mile races, respectively (52% female). Finish times were compared using t tests or Mann–Whitney U tests, a Chi-squared test of independence examined the relationship between sex and ranking, and multivariable linear regressions examined relationships between sex, age, and finish time. There were 96 finishers in the 50 mile race (46% female) and 91 finishers in the 100 mile race (45% female). The median finish time for 50 miles was 12.64 ± 2.11 h with no difference between sexes (1.2%, p = 0.441). However, the top-10 males finished the race ∼85 min faster than the top-10 females (13.8%, p = 0.045). The mean finish time for 100 miles was 31.58 ± 3.36 h with no difference between sexes (3.2%, p = 0.132) and no difference between the top-10 males and top-10 females (4.4%, p = 0.150). Linear and multivariable regression models using sex and age were unable to predict overall finish time in either race. In conclusion, the sex-based performance discrepancy shrinks to 1%–3% in ultramarathons when males and females compete in comparable numbers. Top-performing males still retain a considerable advantage over shorter distances.
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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 | 0.002 |
| 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) | 0.005 | 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".