Enantiospecific Perfluorooctane Sulfonate (PFOS) Analysis Reveals Evidence for the Source Contribution of PFOS-Precursors to the Lake Ontario Foodweb
Bibliographic record
Abstract
Exposure to perfluorooctane sulfonate (PFOS) may arise directly, from emission and exposure to PFOS itself, or indirectly via the environmental release and degradation of PFOS-precursors. Human serum enantiomer fractions (EFs) of 1m-PFOS have been shown to be nonracemic, suggesting that PFOS-precursors are a significant source of PFOS in humans, but little is known about the importance of PFOS-precursors in ecosystems. In the current work, concentrations of PFOS, perfluorooctane sulfonamide (PFOSA), PFOS isomer profiles, and EFs of 1m-PFOS were determined in Lake Ontario water, sediment, fishes and invertebrates. Concentrations of PFOS and PFOSA were highest in slimy sculpin and Diporeia, and concentrations of the two compounds were often correlated. 1m-PFOS was racemic in sediment, water, sculpin and rainbow smelt, but nonracemic in the top predator, lake trout, and all invertebrate species. Furthermore, EFs were correlated with the relative concentrations of PFOS and PFOSA in invertebrates. Overall, these empirical observations with a new analytical tool confirm previous suggestions that PFOS-precursors contribute to PFOS in the food web, likely via sediment. Implications are that future PFOS exposures in this ecosystem will be influenced by an in situ source, and that the apparent environmental behavior of PFOS (e.g., bioaccumulation potential) can be confounded by precursors.
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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 | 0.000 | 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".