Bibliographic record
Abstract
Over 50% of the world’s population lives in cities, many of which are hot, polluted, and expanding. The design of cities impacts local meteorology and climate, which affect building energy use and the comfort and health of urban residents. Numerical models that incorporate the relevant urban elements and physical processes can predict these effects and guide management strategies. Addition of vegetation is a key design strategy for moderation of local urban climate, and many cities already boast extensive vegetation. Relative to shorter vegetation, urban trees have unique effects on local climate and pollutant dispersion: they provide shade and shelter, interacting with buildings and streets to alter climate and wind flow. Urban canopy models (UCMs) predict neighbourhood-scale (10² – 10⁴ m) energy exchange and climate of atmospheric layers between and above the buildings. Few UCMs represent the urban canopy with multiple layers, which permit more flexible and process-based representation of canopy physics. Most UCMs neglect vegetation, or incorporate it with a separate model, neglecting interaction between vegetation and built elements. This dissertation develops BEP-Tree, the first multi-layer urban canopy model that explicitly includes trees and their interaction with buildings. It consists of an existing multi-layer UCM, a foliage energy balance model, and two major developments: firstly, a model that distributes solar and infrared radiation amongst tree foliage, road, roof, and wall elements at multiple heights, accounting for radiation ‘trapping’ and mutual shading; secondly, parameterization of building and tree foliage effects on flow, including generation and dissipation of turbulence, drag on the mean wind, and explicit consideration of sheltering. The combined model permits a range of building and tree configurations, and makes possible advanced assessment of impacts of trees on urban climate, air quality, human comfort and building energy loads. BEP-Tree is compared with measurements from the Sunset neighbourhood in Vancouver, Canada. Urban trees channel sensible heat into latent heat (evaporation), shift surface-atmosphere energy exchange upwards, slow canopy wind, and dissipate turbulence, especially if taller than nearby buildings. Effects of trees on canopy thermal climate depend on representation of neighbourhood-scale foliage clumping in radiation versus dynamical processes, and further theoretical advances are required.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".