Perfluorooctanoic Acid (PFOA): Environmental Sources, Chemistry, Toxicology, and Potential Risks
Bibliographic record
Abstract
Attention regarding perfluoroalkyl and polyfluoroalkyl substances (PFAS) has increased in recent years, due to recognition of widespread environmental presence, recognition of a chemical structure that confers resistance to degradation, and reported health concerns. Historical common exposure sources include food, drinking water, occupational circumstances, and products in commerce (e.g., carpeting, clothing, paper products). Early-2000’s data showed perfluorooctanoic acid (PFOA) was present in blood samples from nearly all the U.S. general population (>99%). Alterations in industrial manufacturing processes, increased regulatory scrutiny, and advanced water treatment options have reduced the reported human body burden of PFAS in the U.S., including for PFOA. Human serum concentrations of PFOA, which do not identify the source(s) of exposure, have exhibited a substantial decrease (~63%) between the 1999–2000 and the 2013–2014 NHANES monitoring by the Centers for Disease Control and Prevention (CDC). With respect to both noncancer and potential cancer effects that may be associated with reported serum levels of PFOA, conclusions regarding an absence of effects, or insufficient information to suggest adverse effects, prevent a consistent conclusion about the occurrence and magnitude of human health effects. In the last several years, a number of state and federal agencies have developed health advisories or guideline values for drinking water exposure that are in the sub-ppb range. In 2016, a USEPA Health Advisory of 0.07 ppb was released, which stands in contrast to 2016 Health-based Values from Health Canada ranging from 0.2 ppb (PFOA) to 30 ppb, and a few other U.S. states with values even less than that of USEPA (e.g., MN, NJ, VT). Further work is necessary to distinguish in transparent fashion between drinking water levels that intentionally are set on a conservative basis to protect human health vs exposure levels that may be associated with tangible adverse effects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".