Urban climate walk: A stop-and-go assessment of the dynamic thermal sensation and perception in two waterfront districts in Rome, Italy
Bibliographic record
Abstract
This study set out to understand the dynamics of human thermal sensation and perception associated with outdoor thermal variability in urban contexts. Previous studies found that compact urban forms and green features can contribute to urban climate diversity, and conjectured whether the wax and wane of thermal stress can promote thermal satisfaction in outdoor public spaces. Hence, a stop-and-go method has been developed to accurately capture thermal transitions along urban walks and to provide snapshots of the momentary body thermal sensation and subjective thermal perception. The measurement campaigns carried in late summer involved a total of 40 participants walking for 70 min through two waterfront districts in Rome, Italy. Our findings indicate that: (1) the oscillation of air temperature along the dense urban walk (R2=0.74) is nearly twice as frequent as that along the sparse suburban walk (R2=0.23), due to the microclimatic diversity shaped by the compact urban fabrics, pocket parks and tree-lined river banks; (2) the Universal Thermal Climate Index (UTCI) contrast can effectively predict thermal alliesthesia (R2=0.34) measured by the rate of change of mean skin temperature (d(Tmskin)/dt < 0.012°C∙min−1); (3) subjective perception shows a significant trend but a poorer model fit (R2<0.25) predicted by UTCI and Tmskin; (4) two confounders, view and social backgrounds, are proved to affect the regression model between the objective and subjective data. The conclusions emphasise the importance of incorporating spatial and social contexts into the investigation of outdoor thermal comfort via physiological and psychological approaches.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".