The importance of air quality for underground spaces: An international survey of public attitudes
Bibliographic record
Abstract
Space is a resource that is constantly being depleted, especially in mega-cities. Underground workspaces (UGS) are increasingly being included in urban plans and have emerged as an essential component of vertical cities. While progress had been made on the engineering aspects associated with the development of high-quality UGS, public attitudes toward UGS as work environments (ie, the public's design concerns with UGS) are relatively unknown. Here, we present the first large-scale study examining preferences and attitudes toward UGS, surveying close to 2000 participants from four cities in three continents (Singapore, Shanghai, London, and Montreal). Contrary to previous beliefs, air quality (and not lack of windows) is the major concern of prospective occupants. Windows, temperature, and lighting emerged as additional important building performance aspects for UGS. Early adopters (ie, individuals more willing to accept UGS and thus more likely to be the first occupants) across all cities prioritized air quality. Present results suggest that (perceived) air quality is a key building performance aspect for UGS that needs to be communicated to prospective occupants as this will improve their attitudes and views toward UGS. This study highlights the importance of indoor air quality for the public.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".