PFOS or PreFOS? Are perfluorooctane sulfonate precursors (PreFOS) important determinants of human and environmental perfluorooctane sulfonate (PFOS) exposure?
Bibliographic record
Abstract
The extent to which perfluorooctanesulfonate precursors (PreFOS) play a role in human or environmental exposure to perfluorooctanesulfonate (PFOS) is not well characterized. The diversity of manufactured PreFOS and its degradation products (e.g. C(8)F(17)SO(2)R and C(8)F(17)SO(2)NR'R'', where R is H or F, and R' and R'' are various) has made it difficult to track their fate. Temporal trends of PFOS in both humans and wildlife are discrepant, thus it is difficult to predict future exposure, and hypotheses about the role of PreFOS have been raised. Although abiotic degradation of commercially important PreFOS materials requires further research, current data suggest that the yield of PFOS is negligible or minor. On the other hand, in vivo biotransformation of PreFOS yields PFOS as the major metabolite, and >32% yields have been observed. In Canadians, exposure to PreFOS was equivalent or greater than direct PFOS exposure prior to 2002. In most ocean water, PFOS is dominant to PreFOS, but in the oceans east of Greenland there may be more PreFOS than PFOS, consistent with the fact that whales and humans in this region also show evidence of substantial PreFOS exposure. Quantitative assessments of PFOS body-burdens coming from PreFOS are complicated by the fact that PreFOS partitions to the cellular fraction of blood, thus biomonitoring in serum under predicts PreFOS relative to PFOS. Many unknowns exist that prevent accurate modelling, thus analytical methods that can distinguish directly manufactured PFOS, from PFOS that has been biotransformed from PreFOS, should be applied in future human and environmental monitoring. Two new source tracking principles are presented and applied to human serum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".