Dataset for: Simulating The Urban Canopy's Impact on Wind-Driven Natural Ventilation
Bibliographic record
Abstract
This dataset was created from coupled indoor-outdoor large eddy simulations (LES) of flow through an idealized urban canopy, as described in the paper "Simulating The Urban Canopy's Impact on Wind-Driven Natural Ventilation". The dataset includes two main files: flowStatsMI.csv provides per-window quantities including ventilation rates and scalar (temperature/tracer) fluxes; roomVentilationMI.csv provides per-room quantities including aggregated window statistics, volume-averaged quantities (e.g., temperature, tracer decay), and surface fluxes (e.g., temperature flux from the ceiling). Together, these datasets offer a detailed characterization of wind-driven natural ventilation across various parameterizations, varying wind conditions, canopy densities, and indoor-outdoor temperature differences. Across these wind and canopy parameterizations, we measure ventilation through single-sided, cross, corner, and dual-room ventilation scenarios. Additionally, all these ventilation conditions were run with and without skylights. The included README.md fully documents all quantities in the dataset.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".