A multi-scalar climatological analysis in preparation for extreme heat at the Tokyo 2020 Olympic and Paralympic Games
Why this work is in the frame
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Bibliographic record
Abstract
Extreme heat can be harmful to human health and negatively affect athletic performance. The Tokyo Olympic and Paralympic Games are predicted to be the most oppressively hot Olympics on record. An interdisciplinary multi-scale perspective is provided concerning extreme heat in Tokyo-from planetary atmospheric dynamics, including El Niño Southern Oscillation (ENSO), to fine-scale urban temperatures-as relevant for heat preparedness efforts by sport, time of day, and venue. We utilize stochastic methods to link daytime average wet bulb globe temperature (WBGT) levels in Tokyo in August (from meteorological reanalysis data) with large-scale atmospheric dynamics and regional flows from 1981 to 2016. Further, we employ a mesonet of Tokyo weather stations (2009-2018) to interpolate the spatiotemporal variability in near-surface air temperatures at outdoor venues. Using principal component analysis, two planetary (ENSO) regions in the Pacific Ocean explain 70% of the variance in Tokyo's August daytime WBGT across 35 years, varying by 3.95°C WGBT from the coolest to warmest quartile. The 10-year average daytime and maximum intra-urban air temperatures vary minimally across Tokyo (<1.2°C and 1.7°C, respectively), and less between venues (0.6-0.7°C), with numerous events planned for the hottest daytime period (1200-1500 hr). For instance, 45% and 38% of the Olympic and Paralympic road cycling events (long duration and intense) occur midday. Climatologically, Tokyo will present oppressive weather conditions, and March-May 2020 is the critical observation period to predict potential anomalous late-summer WBGT in Tokyo. Proactive climate assessment of expected conditions can be leveraged for heat preparedness across the Game's period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 it