Quantitative filter forensics with residential HVAC filters to assess indoor concentrations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analysis of the dust from heating, ventilation, and air conditioning (HVAC) filters is a promising long-term sampling method to characterize airborne particle-bound contaminants. This filter forensics (FF) approach provides valuable insights about differences between buildings, but does not allow for an estimation of indoor concentrations. In this investigation, FF is extended to quantitative filter forensics (QFF) by using measurements of the volume of air that passes through the filter and the filter efficiency, to assess the integrated average airborne concentrations of total fungal and bacterial DNA, 36 fungal species, endotoxins, phthalates, and organophosphate esters (OPEs) based on dust extracted from HVAC filters. Filters were collected from 59 homes located in central Texas, USA, after 1 month of deployment in each summer and winter. Results showed considerable differences in the concentrations of airborne particle-bound contaminants in studied homes. The airborne concentrations for most of the analytes are comparable with those reported in the literature. In this sample of homes, the HVAC characterization measurements varied much less between homes than the variation in the filter dust concentration of each analyte, suggesting that even in the absence of HVAC data, FF can provide insight about concentration differences for homes with similar HVAC systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.003 | 0.004 |
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 it