Modeling the spatial distribution of urban heat risk: a comparative study of two major metropolitan cities in Bangladesh
Bibliographic record
Abstract
Generating maps of potential urban heat risk in metropolitan areas is critical in understanding temperature related risks in the face of climate change. This paper employs a systematic methodological approach to develop urban heat risk models for Khulna and Rajshahi metropolitan areas upon incorporating quantitative data obtained from multiple sources. This method consists of four steps: (i) the utilization of the analytical hierarchical process (AHP) to generate a weights matrix for heat vulnerability and exposure; (ii) the assessment of a heat vulnerability employing indexing method; (iii) the generation of exposure maps using a multi-criteria decision making (MCDM) system; and (iv) the development of a heat risk map by integrating vulnerability and exposure maps using geographical information systems (GIS). Results demonstrate that 3.08 sq km (7.3%) area of Khulna and 1.7 sq km (3.74%) area of Rajshahi are highly susceptible to urban heat. Furthermore, the moderate heat risk zone encompasses 15.68 sq km (37.16%) of Khulna and 17.4 sq km (38.24%) of Rajshahi. This study emphasizes the urgency of incorporating heat related factors in climate adaptation planning, which will help policymakers, planners, and professionals to consider efficient policies derived from scientific evidence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".