Differences in gender and performance in off-road triathlon
Bibliographic record
Abstract
The aims of this study were: (1) to examine performance trends and compare elite male and female athletes at the off-road triathlon (1.5-km swim, 30-km mountain biking, and 11-km trail running) world championships since its inception in 1996, and (2) to compare gender-related differences between off-road triathlon and conventional road-based triathlon. Linear regression analyses and ANOVA were used to examine performance trends and differences between the sexes. Elite male performance times stabilized over the 2005-2009 period, whereas elite female performance times continued to improve, especially for the run leg. Differences in performance times between the sexes were less marked in swimming than in mountain biking and running, whereas differences in power output were more marked for mountain biking than for swimming and running. In addition, differences in cycling between the sexes were greater for off-road than conventional on-road triathlon. The specific aspects of mountain biking (e.g. level and terrain) may partly explain the significant differences between the sexes recorded in cycling for off-road triathlon. Future studies will need to focus on the physiological bases of off-road triathlon and how they differ from conventional triathlon.
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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 | 0.001 | 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".