REVIEW OF METHODS AND TECHNIQUES FOR THE ASSESMENT OF WINDS AT PEDESTRIAN LEVEL
Bibliographic record
Abstract
Wind tunnel tests for assessing Pedestrian Level Winds (PLWs) are usually performed for existing new developments, especially tall buildings.Tall buildings can deflect high velocity upper level winds towards the ground creating high wind speeds at pedestrian level which could affect the comfort and safety of pedestrians.Auckland, Wellington and several other cities such as Tokyo, Karlsruhe, Toronto etc., insist on wind tunnel tests for tall buildings prior to granting approval.Since there is an increase in tall building activity in India, it is now being realized that they need to be assessed for favorable PLWs.With the construction of boundary layer wind tunnels in many cities around the world after the 1960s and the introduction of sophisticated probes and measurement techniques such as the erosion technique, etc., it is now possible to measure wind speeds over large areas.This paper gives an overview of the state of the art wind laboratory methods and techniques that are presently being used for assessing pedestrian level winds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".