Real-time modelling of dangerous wind speed gusts and thermal comfort conditions in campus of Moscow State University (Moscow, Russian Federation)
Bibliographic record
Abstract
Wind speed modeling on microscale can be important not only for local authorities but also for citizens. Due to the heterogeneity of urban development in the Moscow region, wind gusts geography and thermal comfort conditions at different points in the same territory will differ noticeably with the same meteorological parameters. Thus, it is necessary to study such parameters at the microscale. Therefore, within the framework of this study, in order to inform the public about the negative impact of the weather, and further to minimize the consequences on the human body, an attempt was made to develop an operational system for predicting dangerous conditions of wind gusts and thermal comfort. In order to collect climate statistics, climate data were calculated for comfort conditions for the MSU campus using the RayMan model. Wind gusts modeling was performed using ENVI-MET model. Therefore, it is possible to analyze the changes in biometric conditions and wind speed in recent years and track trends in various locations. Since the input parameters for the RayMan diagnostic model, which processes only text documents, serve as predictive data for the Canadian GEM global meteorological parameters in grib2 format, a program for converting files using Command.exe and Fortran-90 language allowed us to create an online module for predicting biometric indices (UTCI, PET and mPET). For the convenience of perception of information, the results of calculations are visualized on the basis of Yandex maps. Research was supported by the grant program of Russian Foundation of Basic Research (project no. 19-35-70009 mol_a_mos ). The work of Pavel Konstantinov, Elizaveta Nikolaeva and Sergey Bukin was supported by Russian Science Foundation (project no. 19-77-30012)
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".