Environmental Aspect Impact on Frameworks Development for Classifying Urban Configuration Indicators
Bibliographic record
Abstract
The urban environment consists of the complex interaction between the physical elements of urban areas and surrounding environmental factors, making it difficult to measure and manage.Urban configuration of the city can substantially influence on the local climate, and this influence varies according to the region's environmental conditions.Therefore, to attain outdoor thermal comfort, it is necessary to understand and study each urban context according to its own environmental conditions.Accordingly, the study aims to analysis the relationship between urban configuration and its thermal environment.To fulfill the research purpose, a simulation was executed utilizing Ecotect software, which assesses environmental performance, concentrating on two distinct scenarios characterized by organic and grid urban designs.The simulation results demonstrated that the organic urban planning achieved superior thermal comfort compared to grid-based planning.This discovery 'indicates that the urban configuration best adapted to hot, arid weather is a compact urban fabric, a defining trait of traditional historical cities.In these designs, building masses are intricately intertwined and overlapping, while streets and pedestrian routes comprise small, winding, and branching lanes.These features offer protection from climatic conditions, diminish direct solar radiation exposure, and generate substantial shadowing, thereby improving thermal comfort.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".