Run-Time and Statistical Pedestrian Level Wind Map for Downtown Toronto
Bibliographic record
Abstract
Rapid population growth and urbanization have led to the development of high-density and high-rise structures around the world. Tall structures in proximity can negatively affect pedestrian comfort by directing strong winds to the ground near the structure. Pedestrian level wind (PLW) may affect local businesses/services, pedestrian comfort and in extreme cases jeopardizes pedestrian safety. The downtown portion of the City of Toronto (∼10 km2) was chosen as the study region due to the recent development of many high-rise structures. The region was split into 10 zones and Computational Fluid Dynamics (CFD) was utilized to study the wind effects of the local building geometry and arrangements. Wind velocities were extracted from Computational Fluid Dynamics and coupled with historical meteorological data from Billy Bishop Airport. The coupled velocities from CFD were found to be within 20 and 5% RMSE, respectively, of the recorded data at the wind station for 85% of the sampled data. Wind velocities were then extracted at various elevations and coupled with meteorological weather data to generate real-time, historical, and statistical visualization of local wind fields. This study enables the prediction of real-time, historical, and statistical wind speeds, bearings at various elevations. Visualization of the flow field provides important insights for pedestrians, architects, engineers, and city planners regarding local wind speeds and identify problematic areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".