Analyzing the Factors and Variables of Heat Island Effect in Comprehensive High-Rise Residential Quarter of Mountain City
Bibliographic record
Abstract
In order to guide the planning of urban microclimate, the influencing variables of the heat island intensity (UII) of tall residential quarter in mountain city in spring and summer is quantitatively analyzed. Through experiments and simulations, 6 variables including wind speed(WS), green ratio(GR), impervious ratio(IR), average surface temperature(AST) , shadow ratio(SR), H / W were chosen and summarized as factors by dimension reduction in factors analysis; further, the dominant variables and factors in different time were confirmed through multiple linear regression analysis of the factors/ variables and UII. Results revealed that in the microclimate tests, it is appropriate to make factor analysis when the cumulative contribution rate of a factor is higher than 50%, and the significant correlated factor of UII is horizontal surface factor (HSF) in three fifths of the testing time in July. The variable analysis revealed that the significance of WS on UII is more in cloudy days than that in sunny days; the significance of SR is higher in summer than that in transition seasons and is most in 15:00, it demonstrates that optimal utilization of the building shadings is an effective way to improve the outdoor thermal environment in residential quarters.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".