Investigating the impact of filters on long-term particle concentration measurements in residences (RP-1649)
Bibliographic record
Abstract
Filters in heating, ventilation, and air-conditioning (HVAC) systems are the most prevalent air cleaning method in residential environments in North America. This study evaluated the long-term impact of residential filtration systems on indoor particle concentrations by examining concentration measurements from low-cost monitors over one year in twenty homes in Toronto, Ontario, Canada. These concentration results suggested that in general, indoor concentration had a similar seasonal trend as the ambient concentration, and indoor activities (e.g., cooking) elevated indoor particle levels for 40-50% of the time. Further, the impacts of electret filters were examined using a non-electret filter with a minimum efficiency reporting value (MERV) of 8 as the reference point at each home. The mean effectiveness of the filters (MERV 8E = −4.19%, MERV 11E = −0.51%, and MERV 14E = 14.5%) were lower than values found in the literature, most likely due to lower HVAC system runtime in our sample of homes (median = 9.6%). Overall, this filter effectiveness analysis reveals that the real-life filter performance was strongly influenced by system and house characteristics (e.g., system runtime, in-situ efficiency, air change rate, and particle source strength), and thus can be different from modeling and laboratory test results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".