Determination of Wind Pattern Inside an Urban Area Through a Mesoscale-Microscale Approach
Bibliographic record
Abstract
The present paper aims to expose the Mesoscale-Microscale numerical approach adopted for studying the air fluxes inside an urban area located in the city of Pescara (Italy). The data, recorded by a real anemometer, are compared with three Mesoscale models (Pleim-Xiu, Blackadar and MRF-LSM), each of them presents five nested domains. On the bases of the monthly values of Root Mean Square Error, BIAS and Standard Deviation, the most accurate mesoscale model is identified and evaluated. On the Microscale side, instead, two cylindrical domains are studied. The first model considers only the topography of the terrain, whereas the other also adds the buildings present inside the investigated area. The domains, with a diameter of 6 [km] and a height equal to 0.5 [km], are studied by assuming the incoming wind from four different directions. Comparisons are then made among wind speeds and directional inflows obtained from the two models. Mesoscale analyses are carried out with the weather forecast software MM5, and Microscale simulations are performed with the commercial software STAR-CCM+.
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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) | 0.001 | 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".