Study on the impacts of urban network evolution on urban wind and heat environment based on improved genetic algorithm
Bibliographic record
Abstract
The irrational layout of urban space is very likely to produce urban heat island (UH). Thus, it is highly necessary to explore how the evolution and spatial distribution of urban network affect the urban wind and heat environment (W&HE). In this paper, an improved genetic algorithm (GA) is proposed to simulate the evolution of urban network, and the UH intensities (UHIs) of Changsha, China are monitored at 18 urban and 7 suburban observation points. On this basis, the author analysed the impacts of urban spatial layout on the W&HE and the UHI. The results show that: the improved GA is feasible for simulation and analysis of the evolution trend of urban network; the UH effect increased with the total urban area and building floor-area ratio (FAR); the mean daytime UHI in downtown Changsha decreased with the growth in green space ratio and increased with the growth in the hardened ground ratio. Therefore, the urban spatial layout should be planned rationally to control the development intensity, lower the ratio of hardened ground and expand the green space in the urban area. The research findings lay a solid theoretical basis for the optimal design of urban layout and the improvement of urban W&HE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".