Microclimate Assessment at Real Experimental Conditions for Green Energy Urban Policy
Bibliographic record
Abstract
The incoming transformation of urban built-up areas, surroundings morphology, and local climate due to global warming connected to the necessity of renewable energy use maximization is the fundamental to the main aim of the present research. Indeed, urban policies can benefit from the accurate monitoring of microclimate variables in order to ensure fine living standards in cities, while improving the energy performance of the built environment. In this view, a systemic experimental approach was implemented. A monitoring campaign using dynamic experimental measurements under real conditions was carried out at an inter-urban scale taking into account different building-plant systems forms and urban configuration. In detail, two innovative portable monitoring systems were used for monitoring key multi-domain parameters at hyperlocal, urban, and intra-urban scale. The monitoring campaign was carried out in summer and winter in the city center of Florence, Italy. Research findings highlighted that urban microclimate control can be a potential factor for urban heat island (UHI) mitigation and sustainable green energy solutions, which involve social, economic, and energy policy beyond environment. The analysis of real microclimate conditions may support the green energy urban policy development in terms of renewable energy integration and urban areas design and management.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".