Mitigating urban heat exposure inequity with a seasonally adaptive machine-learning approach
Bibliographic record
Abstract
Urban heat exposure poses significant risks to human well-being and contributes to emerging social inequity. While previous research has addressed urban heat exposure, mitigating the inequity associated with such hazards remains a significant challenge and an understudied area. To address this issue, this study proposes a machine-learning approach to assess the effectiveness of various urban development strategies in mitigating urban heat exposure inequity. The results from real-world datasets demonstrate that our approach effectively quantifies urban heat inequity, and the proposed machine learning model can accurately capture urban heat exposure based on various urban environmental variables. The season-specific simulation is further conducted by evaluating the outcomes of various urban development strategies across different seasons. The simulation results show that while modifying certain built features may help mitigate heat exposure inequity based on annual data, their effects are significantly more pronounced in winter. In contrast, improving natural features yields more substantial effects in summer and consequently serves as an optimal strategy for mitigating urban heat exposure inequity, which may be overlooked when relying solely on annual data. Compared with prior research that primarily focuses on hazard exposure itself, this study advances the field by addressing the urban heat inequity through a novel and effective machine-learning framework. The proposed methodology not only offers a robust framework for evaluating mitigation strategies but also leverages a season-specific perspective to uncover inefficiencies in approaches that may seem effective in annual analyses yet prove inadequate in critical seasons. By recognizing these seasonal variations, this study enables the development of targeted and cost-effective strategies that optimize urban planning interventions, ultimately reducing urban heat exposure and promoting social equity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".