Perceptions and physical drivers of extreme heat at transit stops
Bibliographic record
Abstract
Extreme urban weather generates unequal impacts on human health and well-being, with transit stops often serving as critical points of exposure to extreme micro-climatic conditions. These locations are essential for urban mobility, particularly for transit-dependent riders, often among the most vulnerable and economically disadvantaged populations. However, transit stops frequently lack adequate protection against heat, leaving riders exposed to potentially hazardous thermal conditions. This study investigates the intersection of biophysical and social dimensions of extreme heat at transit stops in Denver, Colorado, USA.A representative sample of 125 bus stops was selected to capture variability across physical attributes (e.g., vegetation cover, impervious surfaces, and built land cover) and socioeconomic gradients (e.g., income levels and racial/ethnic demographics). Summertime mean radiant temperature was measured at these stops using low-cost weather stations. These observations were used to characterize physical drivers of heat exposure, including micro- and neighborhood-scale land cover interactions, transit infrastructure design, and synoptic weather patterns. In parallel, rider perceptions of heat and its influence on transit behavior were assessed through structured surveys and detailed ridership data from individual bus stops.Preliminary results provide insights into rider experiences and behavior under heat stress and highlight key physical drivers of extreme heat. These findings offer actionable recommendations for transit authorities to evaluate the effectiveness of current shade structures, prioritize infrastructure upgrades, and develop targeted heat mitigation strategies for transit users.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".