Proper Choice Of Urban Canopy Model For Climate Simulations
Bibliographic record
Abstract
The Weather Research and Forecasting model (WRF) is coupled with the three types of Urban Canopy Models (UCMs) to predict heat and moisture fluxes from the canopy to the atmosphere. The three UCMs are slab, single-layer, and multi-layer. The WRF-UCMs are applied to investigate the impacts of summer heat on urban climate and characterize the heat island intensity in the Greater Toronto Area (GTA) during the 2011 heat wave period (17th-21st July). The WRF-UCMs are evaluated using simulated hourly air temperature and wind speed results with measurements obtained from various weather stations across the domain of interest. The multi-layer of the urban canopy model (ML-UCM) predicts air temperature and wind speed more accurately comparing to other UCMs. The ML-UCM accounts for the turbulence and multi-reflection within the urban canopy and increases the computation time 30-40% compared to other canopy models (single and slab model).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".