MB2 Urban Environment 1
Bibliographic record
Abstract
The predictive capabilities of a building-resolving prognostic numerical simulation model (urbanSTREAM) for small-scale (microscale) atmospheric flows in an urban environment will be evaluated based on detailed comparisons between the predictions and measurements of various flow quantities obtained in the Joint Urban 2003 (JU2003) field experiment in Oklahoma City.The prognostic model for the wind field in a cityscape is obtained by solving the unsteady Reynolds-averaged Navier-Stokes (URANS) and partially-resolved Navier-Stokes (PRNS) equations.For URANS, a two-equation k-s turbulence closure model is used.However, in contrast to conventional large-eddy simulation (LES), which is based on spatial filtering of the NS equation, PRNS solves the time-filtered NS equation.The latter approach provides a unified framework for the numerical simulation of turbulent flows, and includes URANS, LES and direct numerical simulation (DNS) as special cases, depending on how the cut-off frequency of the filter is chosen.A two-equation k-s PRNS is adopted here, with the eddy viscosity being multiplied by a resolution control parameter function which is dependent on the cut-off wave number (or, equivalently, the cut-off frequency) of the filter.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".