The effects of temperature, humidity, and barometric pressure on short-sprint race times
Bibliographic record
Abstract
A numerical model of 100 m and 200 m world class sprinting performances is modified using standard hydrodynamic principles to include effects of air temperature, pressure, and humidity levels on aerodynamic drag. The magnitude of the effects are found to be dependent on wind speed. This implies that differing atmospheric conditions can yield slightly different corrections for the same wind-gauge reading. In the absence of wind, temperature is found to induce the largest variation in times (0.01 s per 10°C increment in the 100 m), while relative humidity contributes the least (under 0.01 s for all realistic conditions for 100 m). Barometric pressure variations at a particular venue can also introduce fluctuations in performance times on the order of a 0.01 s for this race. The combination of all three variables is essentially additive, and is more important for head-wind conditions that for tail-winds. As expected, calculated corrections in the 200 m are magnified due to the longer duration of the race. The overall effects of these factors on sprint times can be considered a ``second order'' adjustment to previous methods that rely strictly on a venue's physical elevation, but can become important in extreme conditions.PACS Nos.: 01.80, 02.60L
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".