Lagrangian simulation of wind transport in the urban environment
Bibliographic record
Abstract
Abstract Fluid element trajectories are computed in inhomogeneous urban‐like flows, the needed wind statistics being furnished by a Reynolds‐averaged Navier–Stokes (RANS) model that explicitly resolves obstacles. Performance is assessed against pre‐existing measurements in flows ranging from the horizontally uniform atmospheric surface layer (no buildings), through regular obstacle arrays in a water‐channel wall shear layer, to full‐scale observations at street scale in an urban core (the Oklahoma City tracer dispersion experiment Joint Urban 2003). Agreement with observations is encouraging, e.g. for an Oklahoma City tracer trial in which sixteen detectors reported non‐zero concentration, modelled concentration lies within a factor of two of the corresponding observation in nine cases (FAC2 = 56%). Although forward and backward simulations offer comparable fidelity relative to the data, interestingly they differ (by a margin far exceeding statistical uncertainty) wherever trajectories from source to receptor traverse regions of abrupt change in the Reynolds stress tensor. Copyright © 2009 Royal Meteorological Society
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".