Observed determinants of urban outdoor thermal exposure during hot summer and snowy winter periods in a humid continental climate
Bibliographic record
Abstract
• Mobile biometeorological station MaRTy used for data collection in Guelph, Canada • Mean radiant temperature determined from 6-direction shortwave and longwave radiation • Shade strongly reduces daytime heat exposure but slightly increases it at night • Snow cover in winter generates solar reflection warming and longwave cooling at night • Recommendations for street design and maintenance for both hot and cold weather Many cities in the midlatitudes experience both extreme heat and cold, and pedestrians are exposed to thermal extremes that cause bodily stress. With growing urban populations, city design that contributes to mitigating summer heat while reducing winter cold exposure is increasingly important. Pedestrian thermal exposure depends on several microclimatic factors, including shortwave and longwave radiation absorption, which can be quantified by the mean radiant temperature ( T mrt ). Limited research has been conducted on the radiative components of thermal exposure in hot, humid summers and cold, snowy winters. We gathered micrometeorological data from diverse urban sites and in multiple seasons in Guelph, Canada, using a mobile human-biometeorological weather station ( MaRTy cart) that applies the six-directional method to determine T mrt . Seasonal datasets were analysed and compared to examine the drivers of thermal exposure and recommend strategies for mitigating heat and cold stress. In summer, shade is the primary factor that reduces daytime heat exposure and it slightly increases nighttime heat exposure. Enhanced pervious ground cover is a secondary factor day and night. In winter, reduced shade alleviated daytime cold exposure, while snow cover provided daytime benefits from increased solar reflections and post-sunset penalties associated with reduced longwave radiation from low snow surface temperatures.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".