Microclimate Analysis as a Design Driver of Architecture
Bibliographic record
Abstract
In the context of global climate change, it is increasingly important for architects to understand the effects of their interventions on indoor and outdoor thermal comfort. New microclimate analysis tools which are gaining appreciation among architects enable the assessment of different design options in terms of biometeorological parameters, such as the Universal Thermal Climate Index (UTCI) and the Outdoor Thermal Comfort Autonomy. This paper reflects on some recent experiences of an architectural design office attempting to incorporate local climatic considerations as a design driver in projects. The investigation shows that most of the available tools for advanced climatic modelling have been developed for research purposes and are not optimized for architectural and urban design; consequently, they require adaptations and modifications to extend their functionality or to achieve interoperability with software commonly used by architects. For this scope, project-specific Python scripts used to extract design-consequential information from simulation results, as well as to construct meteorological boundary conditions for microclimate simulations, are presented. This study describes the obstacles encountered while implementing microclimate analysis in an architectural office and the measures taken to overcome them. Finally, the benefits of this form of analysis are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".