Assessing Future Changes in Mean Radiant Temperature: Considering Climate Change and Urban Development Impacts in Fredericton, New Brunswick, Canada, by 2050
Bibliographic record
Abstract
Urban development and climate change are two main impacting factors in the thermal environment of cities. This study aims to analyze future changes in Mean Radiant Temperature (MRT), one of the main contributors to human thermal comfort and the concept of Urban Heat Island (UHI), considering climate change and urban development scenarios in the study area, Fredericton, New Brunswick, by 2050. The analysis utilizes the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model from the Urban Multi-scale Environmental Predictor (UMEP) platform to calculate MRT values. By integrating these two impacting factors, this research provides insights into the potential future changes in MRT levels and the resulting thermal conditions and geohazards in the study area. The analysis enables the identification of areas susceptible to increased radiant heat exchange due to the proposed changes in land cover, urban morphology, and air temperature. Furthermore, this study contributes to a better understanding of the complex interactions between climate change, urbanization, and urban microclimates. By incorporating MRT assessments and prioritizing thermal comfort, cities can develop strategies to mitigate the negative effects of UHI and create sustainable and livable urban environments for future generations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".