Knowledge-based Desk-top Analysis of Pedestrian Wind Conditions
Bibliographic record
Abstract
Since 1960's, pedestrian wind conditions around buildings have been studied extensively through wind-tunnel testing, full-scale measurements, numerical simulation and other approaches. These studies have created a broad knowledge base for pedestrian wind conditions around different building configurations. In many situations, the knowledge base allows for a reliable desk-top estimation of pedestrian wind conditions around new developments without wind-tunnel testing. A typical desk-top analysis may require a direct or indirect use of information from wind-tunnel measurements. The paper describes several effective methods of desk-top analysis currently used in the field of pedestrian wind studies. The focus of this paper, however, is on an innovative computer program developed as a time and cost-efficient alternative to the traditional approaches. The computer program has been updated through consulting practice and has proved to be reliable, consistent and efficient. A desk-top analysis is particularly useful at the preliminary stage of building design, when different building dimensions, orientations and configurations can be evaluated for improving pedestrian wind conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".