Airborne Precursors Predict Maternal Serum Perfluoroalkyl Acid Concentrations
Bibliographic record
Abstract
Human exposure to persistent perfluoroalkyl acids (PFAAs), including perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorooctanesulfonate (PFOS), can occur directly from contaminated food, water, air, and dust. However, precursors to PFAAs (PreFAAs), such as dipolyfluoroalkyl phosphates (diPAPs), fluorotelomer alcohols (FTOHs), perfluorooctyl sulfonamides (FOSAs), and sulfonamidoethanols (FOSEs), which can be biotransformed to PFAAs, may also be a source of exposure. PFAAs were analyzed in 50 maternal sera samples collected in 2007-2008 from participants in Vancouver, Canada, while PFAAs and PreFAAs were measured in matching samples of residential bedroom air collected by passive sampler and in sieved vacuum dust (<150 μm). Concentrations of PreFAAs were higher than for PFAAs in air and dust. Positive associations were discovered between airborne 10:2 FTOH and serum PFOA and PFNA and between airborne MeFOSE and serum PFOS. On average, serum PFOS concentrations were 2.3 ng/mL (95%CI: 0.40, 4.3) higher in participants with airborne MeFOSE concentrations in the highest tertile relative to the lowest tertile. Among all PFAAs, only PFNA in air and vacuum dust predicted serum PFNA. Results suggest that airborne PFAA precursors were a source of PFOA, PFNA, and PFOS exposure in this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".