Freezing and Frostbite on Mount Everest: New Insights into Wind Chill and Freezing Times at Extreme Altitude
Bibliographic record
Abstract
Cold injury is an acknowledged risk factor for those who venture into high altitude regions. There is, however, little quantitative information on this risk that can be used to implement mitigation strategies. Here we provide the first characterization of the risk of cold injury near the summit of Mount Everest. This is accomplished through the application of a meteorological dataset that has been demonstrated to characterize conditions in the region as inputs to new parameterizations of wind chill equivalent temperature (WCT) and facial frostbite time (FFT). Throughout the year, the typical WCT near the summit of Everest is always <-30°C, and the typical FFT is always less than 20 min. During the spring climbing season, WCTs of -50°C and FFTs of 5 min are typical; during severe storms, they approach -60°C and 1 min, respectively; values typically found during the winter. Further, we show that the summit barometric pressure is an excellent predictor of summit WCT and FFT. Our results provide the first quantitative characterization of the risk of cold injury on Mount Everest and also allow for the possibility of using barometric pressure, an easily observed parameter, in real time to characterize this risk and to implement mitigation strategies. The results also provide additional confirmation as to the extreme environment experienced by those attempting to summit Mount Everest and other high mountains.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".