Response of Outdoor Thermal Environmentto Small Changes in Three-Dimensional UrbanMorphology – a Case Study of Adding Elevatorsto a Residential Quarter in Xuzhou City, China
Bibliographic record
Abstract
Three-dimensional urban morphology plays a role in adjusting the outdoor thermal environment while the effects of little variation in shape on outdoor thermal environment are still not evaluated.In addition, the renovation for senior people is getting more attention in China, which could be developed by installing elevators in old residential quarters.However, the evaluation of installing elevators in the perspective of outdoor thermal environment remains rare.Based on these two issues, 5 different parameters describing the three-dimensional urban morphology were chosen to simulate 4 scenarios and to analyze.Based on the results of one-way ANOVA and stepwise regression, installing elevator changed the three-dimensional shape of residential quarter, but the variation was less than 7%; temperature (T a ), mean radiation temperature (MRT), wind velocity (WV) and predictive mean vote (PMV) changed a little consequently, leading to very limited impacts on outdoor thermal qualities.The pattern of outdoor thermal environment slightly changed when installing elevators in the residential quarter, especially in the situation at night (no solar radiation).From the overall perspective of outdoor thermal environment evaluation, adding elevators in residential quarter has little effect on the threedimensional urban morphology and outdoor thermal environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".