The Cooling Effects of Campus Greenery at the University of British Columbia Campus
Bibliographic record
Abstract
Heat waves have threatened many people’s health and affected people’s thermal comfort on the West Coast of Canada in recent years. Urban greenery is crucial in mitigating climate change issues by influencing the temperature in cities. Land surface temperature is a metric to understand energy change at a landscape scale, while the air temperature is not representative of its surroundings. Land surface temperature can be measured and calculated by using remote sensing technology. This technology can capture surface temperature data from satellite imagery, allowing for accurate measurements of temperature changes over time. The paper studied how urban greenery coverage affected land surface temperature and air temperature respectively at the University of British Columbia in July and August 2022. The study calculated land surface temperature from Landsat images and collected air temperature data from available sensors. Two linear regression models were built to illustrate relationships between urban greenery coverage and land surface temperature and air temperature respectively. The results revealed that with the increasing percentage of urban greenery coverage, the land surface temperature reduced. Deciduous trees showed the strongest ability to reduce land surface temperature, followed by modified grass-herb and coniferous trees. Areas that are classified as barren had the highest land surface temperature. However, there was no significant relationship between urban greenery coverage and air temperature. This study provides valuable information for urban planners and policymakers to design and implement effective green space strategies that can address climate change and human thermal comfort issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".