Walls of Air: A Retrofit for Equitable Indoor Air Quality
Bibliographic record
Abstract
Poor indoor air quality and lack of space for adequate isolation within postwar towers increase the risk of negative effects on resident health leaving them more vulnerable to infectious diseases. The unequal ability to isolate safely within residential spaces during the Covid-19 pandemic is worsened by the assumption that isolation is a purely spatial issue.Ventilation and filtration mitigation strategies are already common in spaces with a high risk of contaminant spread, such as hospitals and labs, however, their active use in preventing the spread of infectious diseases in residential spaces is less common. Recognition of the airborne nature of the Covid-19 virus strengthened the importance of focusing on indoor air quality and airflow management to hinder the spread of the virus while designing a flexible space for living and isolation. \n \nWhile working parallel to the ever-changing information about Covid-19, an analysis of domiciliary Covid-19 mitigation strategies, and a discussion of overcrowding, tower renewal, and air quality in relation to health narrowed the scope of research to exploring how the retrofit of postwar towers can improve occupant health and well-being. This thesis expands upon the agenda of the Tower Renewal Partnership with a postwar tower retrofit that incorporates flexible living spaces within units while prioritizing occupant physical and mental health through a focus on air quality and management to decrease occupant vulnerability to the spread of infectious disease. The versatile components of the retrofit design allow for ease of application across Toronto’s postwar tower stock and thereby provide over half a million people with strategies to maintain good indoor air quality and improve occupant health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".