Assessment of Green Roof Suitability by Active Remote Sensing of University of British Columbia Buildings
Bibliographic record
Abstract
Green roofs are a promising mitigation tool against environmental concerns caused by increased urbanization. This study aims to address which of the University of British Columbia’s buildings are most suitable for a retrofitted green roof using active remote sensing. Through the assessment of six characteristics of buildings, a suitability score was given to all buildings within the area of interest on campus. The six suitability criteria are rooftop slope, rooftop area, building usage, building ownership, structural materials, and light intensity. The six suitability characteristics where selected based off literature on existing green roof retrofitting analyzing their structure, types and performance. This study is part of the Social Ecological Economic Development Studies (SEEDS) Sustainability Program to address sustainability policies and practices on UBC’s campus. The building suitability analysis resulted in the detection of five buildings with the greatest suitability score and their total rooftop area being 31,940.80 m^2. Each of the five buildings then was further investigated in terms of the vegetation health and density surrounding the buildings to isolate potential concerns of planting in the area. The mean NDVI of existing greenery surrounding the top five suitable buildings is 0.67, indicative of moderate to high density vegetation. Implementing green roof retrofitting on the suitable UBC buildings requires an additional accessibility analysis to maximize the positive social impacts of increasing campus greenery and make decisions on the green roof structure.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.003 | 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".