ENVIRONMENT AND BEHAVIOR / March 2001Zacharias et al. / MICROCLIMATE AND DOWNTOWN OPEN SPACE MICROCLIMATE AND DOWNTOWN OPEN SPACE ACTIVITY
Bibliographic record
Abstract
ning from the Université de Montréal. He is currently associate professor and director of the urban studies program at Concordia University. He has published in the fields of environment behavior relationships, urban design, and environmental aesthetics. TED STATHOPOULOS received his civil engineering diploma from the National Technical University of Athens, Greece, and both his M.E.Sc. and Ph.D. from the Uni-versity of Western Ontario. He is currently professor and director of the Centre for Building Studies at Concordia University. He has carried out research and published extensively in the areas of wind effects on buildings and environmental aerodynamics. HANQING WU received his B.Sc. (mechanics) and M.Sc. (fluid mechanics) from Beijing University, China, and his Ph.D. (building studies) from Concordia Univer-sity, Canada. He is now technical director in microclimate studies at RWDI, Inc., spe-cializing in pedestrian-level wind, thermal comfort, air quality, rain infiltration, snow drifting, and accumulation. ABSTRACT: Microclimatic conditions in business district open spaces tend to be more extreme than prevailing weather conditions. Although the buildings are chiefly
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".