Real-time microscale modeling of thermal comfort conditions in Moscow region
Bibliographic record
Abstract
Abstract Urban climate comfort is an indicator of a set of parameters, such as temperature, humidity, and solar radiation for a person’s sensation of being favorable to being outdoors or indoors. In this study, an attempt to develop a technology of real-time prediction of thermal comfort conditions in urban landscape is described (based on the example of Moscow State University campus). For this, the authors used a RayMan model-based algorithm for calculating three most popular worldwide comfort indexes. In the scripting method, predictive data of the Canadian global model meteorological parameters are automatically transferred to the RayMan-model (with an implementation of the unique thermal and radiation properties of the Moscow State University campus landscape) by using an autoclicker software. For the convenience of perception of the information, the results of calculations are visualized on the basis of a free web mapping service. Thus, the main idea of the work is that any user with minimum expenditure of his time resource and without knowledge of the model’s work can launch the program and receive an individual forecast of comfort conditions for the next few hours in a visually understandable format. It is suggested that the developed methodology will be used for calculations on projected areas to identify the safest construction option. Such realtime forecasting will continue to be of particular importance for urban infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".