Per-\nand Polyfluoroalkyl Substances in Dust Collected\nfrom Residential Homes and Fire Stations in North America
Bibliographic record
Abstract
Over the past few years, human exposure\nto per- and polyfluoroalkyl\nsubstances (PFAS) has garnered increased attention. Research has focused\non PFAS exposure via drinking water and diet, and fewer studies have\nfocused on exposure in the indoor environment. To support more research\non the latter exposure pathway, we conducted a study to evaluate PFAS\nin indoor dust. Dust samples from 184 homes in North Carolina and\n49 fire stations across the United States and Canada were collected\nand analyzed for a suite of PFAS using liquid and gas chromatography–mass\nspectrometry. Fluorotelomer alcohols (FTOHs) and di-polyfluoroalkyl\nphosphoric acid esters (diPAPs) were the most prevalent PFAS in both\nfire station and house dust samples, with medians of approximately\n100 ng/g dust or greater. Notably, perfluorooctanesulfonic acid (PFOS),\nperfluorooctanoic acid (PFOA), perfluorohexane sulfonate, perfluorononanoic\nacid, and 6:2 diPAP were significantly higher in dust from fire stations\nthan from homes, and 8:2 FTOH was significantly higher in homes than\nin fire stations. Additionally, when comparing our results to earlier\npublished values, we see that perfluoroalkyl acid levels in residential\ndust appear to decrease over time, particularly for PFOA and PFOS.\nThese results highlight a need to better understand what factors contribute\nto PFAS levels in dust and to understand how much dust contributes\nto overall human PFAS exposure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".