THE INDOOR ENVIRONMENTAL QUALITY PERFORMANCE OF GREEN LOW-INCOME SINGLE-FAMILY HOUSING
Bibliographic record
Abstract
Abstract There is little empirical evidence in the literature about the indoor environmental quality performance of residential buildings in general and of social housing in particular. To address this problem, this study used a mixed-method approach to evaluate the indoor environmental quality performance of 17 green low-income single attached family houses in Brandon, Manitoba, Canada. Questionnaires were administered to occupants to assess their snapshot and long-term satisfaction with the indoor environment. In addition, snapshot measurements were carried out to evaluate the indoor environmental quality factors of thermal comfort, indoor air quality, lighting and acoustics. Occupants' snapshot satisfaction was categorized into two groups (i.e. satisfied/comfortable or dissatisfied/uncomfortable) and compared with snapshot measurements. The results showed the measured IEQ parameters were well below recommended threshold levels. Further, occupants with higher snapshot satisfaction were generally exposed to relatively lower levels of indoor pollutants. A statistically significant difference was found in PM10 level only between the snapshot satisfied and snapshot dissatisfied groups of occupants. Apparent sound transmission classes were below the standard reference value of 50, suggesting potential problems in noise attenuation within different spaces in each apartment and between apartments. The findings of this study could help governments implement green shadowing for public-housing and also renovate existing houses using the same principles.
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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 | 0.000 | 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 | 0.000 |
| 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".