Impact of Weather on Marathon-Running Performance
Bibliographic record
Abstract
UNLABELLED: Marathon running performance slows in warm weather conditions, but the quantitative impact of weather has not been established. PURPOSE: To quantify the impact of weather on marathon performance for different populations of runners. METHODS: Marathon results and weather data were obtained for the Boston, New York, Twin Cities, Grandma's, Richmond, Hartford, and Vancouver Marathons for 36, 29, 24, 23, 6, 12, and 10 yr, respectively. The race results were broken into quartiles based on the wet-bulb globe temperature (Q1 5.1-10 degrees C, Q2 10.1-15 degrees C, Q3 15.1-20 degrees C, and Q4 20.1-25 degrees C). Analysis of the top three male and female finishers as well as the 25th-, 50th-, 100th-, and 300th-place finishers were compared with the course record and then contrasted with weather. RESULTS: Marathon performances of top males were slower than the course record by 1.7 +/- 1.5, 2.5 +/- 2.1, 3.3 +/- 2.0, and 4.5 +/- 2.3% (mean +/- SD) for Q1-Q4, respectively. Differences between Q4 and Q1, Q2, and between Q3, and Q1 were statistically different (P < 0.05). The top women followed a similar trend (Q1 3.2 +/- 4.9, Q2 3.2 +/- 2.9, Q3 3.8 +/- 3.2, and Q4 5.4 +/- 4.1% (mean +/- SD)), but the differences among quartiles were not statistically significant. The 25th-, 50th-, 100th-, and 300th-place finishers slowed more than faster runners as WBGT increased. For all runners, equivalence testing around a 1% indifference threshold suggests potentially important changes among quartiles independently of statistical significance. CONCLUSION: There is a progressive slowing of marathon performance as the WBGT increases from 5 to 25 degrees C. This seems true for men and women of wide ranging abilities, but performance is more negatively affected for slower populations of runners.
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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 | 0.000 |
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".