Performance trends in age‐group runners from 100 m to marathon—The World Championships from 1975 to 2015
Bibliographic record
Abstract
This study examined changes in performance in age-group track runners across years from 1975 to 2015 for 100, 200, 400, 800, 1500, 5000, 10 000 m, and marathon and the corresponding sex differences. Athletes were ranked in 5-year age-group intervals from 35-39 to 95-99 years. For all races and all years, the eight female and male finalists for each age-group were included. Men were faster than women and this observation was more pronounced in the shorter distances. The younger age-groups were faster than the older age-groups and age exerted the largest effect on speed in 800 m and the smallest in marathon. There was a small variation of speed by calendar years. The competition density varied by sex and race distance. Half of participants were from USA, Germany, Australia, and Great Britain, but the participants' nationality varied by sex and race distance. In summary, the variation of competitiveness by sex in short race distances might be important for athletes and coaches. Considering the event's competitiveness and that athletes are participating in both 100 and 200 m or in 200 and 400 m, master women should be oriented to 200 m and master men should be oriented to 100 and 400 m.
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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 | 0.001 |
| 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) | 0.001 | 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".