Urban heat islands in transit-oriented development designated areas in a high-latitude city - Edmonton, Canada
Bibliographic record
Abstract
Transit-oriented developments (TODs), as a smart growth policy, have gained popularity as a way to combat the negative effects of urban sprawl. TODs are also purported to have both environmental and socio-economic benefits. However, little or no research exists regarding their environmental impact, specifically in high-latitude cities. This study aims to bridge the knowledge gap in the literature by analyzing the relationship between TODs and the urban heat island (UHI) effect, which is an environmental phenomenon that results in high temperatures in urban areas. We studied seven transit stations in the city of Edmonton, Canada designated as sites of transition to TODs, to determine the extent of UHI effects in TODs in high-latitude cities. Our results show a significant UHI effect in Edmonton’s TOD-designated (TODD) areas over the last decade compared to non-TODD areas. The variation was mainly linked to the reduced vegetation cover at the expense of increasing developments. Although non-TODD areas also experienced an increase in temperature, the rate of increase in land surface temperature (LST) and UHI effect was higher in the select TODD areas. Our findings suggest urban planners should consider UHI mitigation strategies such as preserving or increasing natural landscape as a key requirement to developing and designing the newly built forms in TODD areas.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".