Modeling Tree Shade Coverage: Planting Recommendations for Optimizing Shade on the University of British Columbia Vancouver Campus
Bibliographic record
Abstract
Tree shade in urban environments serves to improve human thermal comfort and building energy use during the summer months. Under future climates, tree shade becomes an even more important ecosystem service that promotes climate change resilience. Tree shade was modelled in order to optimize shade coverage on the University of British Columbia (UBC) Vancouver campus by providing recommendations for tree planting based on species and planting configuration. Using Light Detection And Ranging (LiDAR) point clouds of 463 individual trees from species considered resilient to climate change, median normalized shade area per species was modelled over the course of one day. Furthermore, the total tree shade for each neighbourhood on campus was modelled using the same LiDAR dataset in order to determine which planting configurations most effectively shaded buildings. The planting configurations of neighbourhoods with a higher percentage of total tree shade falling on buildings were examined in order to provide recommendations for future planting efforts. Deodar cedar and black pine were observed to provide the highest normalized median shaded area, and are recommended for planting in order to promote climate change resilience and shade cover on UBC campus. University Boulevard and Chancellor Place were found to be the neighbourhoods with the most efficient planting configurations for the purpose of shading buildings. This was due to trees planted in thin belts along the south-west and south-east faces of buildings, maximizing individual shade contributions from each tree along the sun-facing side of each building. This tree planting configuration is recommended for the optimal shading of important targets on UBC campus, especially when resources are limited and must be properly allocated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".