Towards mapping the risk for urban heat island: new methods for the analysis of the urban environment
Bibliographic record
Abstract
Urban environment is characterized by some typical features: high density of population and buildings, high energy consumption and shortage of green areas. A main consequence is the Urban Heat Island phenomenon (UHI) that is the systematic higher air temperature of urban environment with respect to rural one. Depending on the climate type, UHI may be welcome (in winter it may reduce heating loads) or, conversely, in warmer climate may increase cooling loads and also mortality rates. UHI has been studied worldwide (Athens, London, Berlin, Vancouver, Montreal, New York, Tokyo, Hong Kong for example) since the sixties of the past century. In Italy, only few studies are available for some big cities like Bologna, Milan, Florence and Rome. Very few data are available concerning the presence of the urban heat island phenome non in medium size cities, the most diffuse in Italy (Modena and Trento for example), none in the Veneto Region in the North East of Italy. In this paper the very first activities directly developed by the authors’ research group of the University of Padua within the frame of the European Project “UHI” are described, concerning both experimental measurements, data analysis and simulation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".