Seasonal land surface temperatures of local climate zones in a high-latitude city in Canada
Bibliographic record
Abstract
This study aims to examine the spatiotemporal variations in winter and summer land surface temperatures (LSTs) in different local climate zones (LCZs) in a high-latitude city. The study focuses on Edmonton, Canada, located between the 53rd and 54th parallels. Using Landsat images and building data, the study examines LST variations and the impact of buildings, vegetation, and snow cover on LST across different LCZs. Results indicate significant spatial clustering and increased summer and winter LSTs across the city, demonstrating a discernible influence of the LCZ type. LCZs are characterized by compact residential structures and heavy industrial use exhibited the highest LSTs. In contrast, open, low-rise, and sparsely built areas with vegetation exhibited lower LSTs. Strong negative correlations have been identified between LCZ’s summer LST and vegetation cover (r = −0.6), as well as winter LST and snow cover (r = −0.5). LCZs characterized by compact built-up, particularly in the city core, showed higher LSTs with reduced snow cover. The building area density (r = 0.8) and building volume density (r = 0.6) demonstrated a strong positive correlation with the average summer and winter LSTs. These results underscore the need for developing local climate-specific strategies to create healthier, more sustainable, and thermally resilient winter cities.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".