Microclimate and Downtown Open Space Activity
Bibliographic record
Abstract
Microclimatic conditions in business district open spaces tend to be more extreme than prevailing weather conditions. Although the buildings are chiefly responsible for this inclemency, their shapes and arrangement could also potentially be used to moderate or enhance prevailing conditions. To provide better guidance in design, we need to know how humans respond to microclimatic conditions. In particular, we need to know first how sunlight, temperature, humidity, and wind combine in sensations of outdoor human comfort, and second, how important microclimatic factors are in behavior. This article reports on a study of revealed preferences for certain local climatic conditions, measured in terms of presence levels and activities in seven closely spaced corporate plazas and public squares in a built-up, downtown area. The observations were conducted over a 5-month period. The measured microclimatic conditions accounted for most of the variance in activity levels and types. Temperature was the single most important variable. Although great variation in level of use among spaces cannot be explained solely in terms of microclimatic differences, use within spaces varies chiefly as a function of microclimate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".