Weather Conditions for Optimal Marathon Performance
Bibliographic record
Abstract
Laboratory studies have identified meteorological factors that affect human endurance performance but cannot completely mimic race situations. Field studies examining the marathon footrace (42 km) have drawn similar conclusions about the impact of weather on performance, but none have identified the meteorological conditions for optimal performances. PURPOSE: 1) To identify and describe the climatic conditions of the fastest men's and women's marathons ever run, 2) to compare world record and Olympic marathon performances as a function of weather, and 3) to compare elite performances between men and women to further characterize potential gender effects. METHODS: Finish time data were obtained for the 10 all-time fastest marathon individual times; world record marathons; Olympic Marathons; and course record performances at Boston, New York, Twin Cities, Grandma's, Richmond, Hartford, and Vancouver Marathons. Weather conditions for the date, duration, and location of these races were analyzed as correlates. RESULTS: All marathon performances examined (men and women combined excluding Olympic marathons) were run in cool conditions (WBGT = 12.0 °C ± 0.42°C, mean ± SE) and 82% of these races were run in clear or scattered cloudy conditions which is higher than that found for all regular marathon races (67.8%). In contrast, Olympic races were run in a WBGT of 19.6 ± 0.96°C (men) and 22.8 ± 0.98 °C (mean ± SE) (women) and the men's and women's winners were on average 2.8 ± 0.6 % and 4.4 ± 1.2 % (mean ± SE) slower than existing world record times with similar sky conditions. The performance decrements observed in the Olympic marathons are in agreement with the slowing expected based on WBGT and runner ability. CONCLUSION: Cool conditions (WBGT = 12°C) with clear to scattered cloudy skies appear representative of optimal marathon performance for both men and women. When environmental conditions become dramatically warmer running time is compromised and the magnitude of slowing is consistent with previous quantitative measures.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".