Explicating the Urban Heat Island phenomenon using in-situ sensors
Bibliographic record
Abstract
Climate change, defined as long-term changes in global temperature and weather patterns primarily due to anthropogenic activities, affects ecosystems, biodiversity, and human health.As a result, urban populations are particularly at risk of heat-related illnesses, exacerbated by the Urban Heat Island (UHI) effect.Most UHI studies have utilized remotely sensed temperature data due to their easy availability and accessibility, but they only detect surface temperature differences and have been shown to portray urban areas as warmer than they actually are.In contrast, in-situ sensors can measure ambient air temperature, which impacts thermal comfort and is directly related to cardiovascular mortality.Currently, UHI can only be accurately measured using conventional in-situ weather stations, such as those operated by Environment and Climate Change Canada (ECCC).However, newer crowdsourced and Volunteered Geographic Information (VGI) approaches, such as using low-cost sensors, when combined with geographic technologies, have the potential to usher in a new era of micro-scale climate studies.This thesis aims to assess the effectiveness of in-situ sensors in capturing and estimating UHI intensity within Canada.Through an extensive review of literature, the advantages of conventional and crowdsourced in-situ temperature data sources over other sources are identified.Furthermore, the challenges of utilizing data from in-situ sensors for UHI studies are also analysed.The analysis highlights the importance of considering spatial representativeness of in-situ sensors, whether conventional or crowdsourced, due to its influence on estimating UHI intensity.Overall, this thesis expands the current understanding of utilizing in-situ sensors to study UHI dynamics, which benefits policy-making and urban planning initiatives that aim to mitigate the adverse impacts of UHI and improve the resilience of cities to climate change.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".