Intervention field study in the Canadian arctic: Improving ventilation, indoor air quality, and the respiratory health in Nunavik dwellings and children
Bibliographic record
Abstract
Abstract Homes with inadequate ventilation and indoor air quality (IAQ) are particularly common in northern and remote communities. Previous studies have observed that the indoor air in these homes can have elevated concentrations of CO2, environmental tobacco smoke, and elevated relative humidity leading to mold issues. These conditions may cause various health problems, such as compromised respiratory health for the occupants and in particular in children with developing respiratory systems. The objectives of this current study were to measure the effectiveness of a targeted optimization of existing heating and ventilation systems at improving ventilation, IAQ, and the respiratory health of children. This study enrolled homes with children under the age 10 in both an intervention group and control group over the winter and spring of 2017-18 in Kuujjuaq, Québec, Canada. Various IAQ, ventilation, and behavioural characteristics were measured both before and after the intervention. Following the intervention, we observed statistically significant reductions in the median values a number relevant IAQ parameters. This study demonstrated that targeted preventative maintenance and optimization of ventilation systems can significantly improve ventilation rates and IAQ.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".