Spatial Trends and Health Risks of Per- and Polyfluoroalkyl Substances in San Francisco Bay Fish from 2009 to 2019
Bibliographic record
Abstract
Consumption of contaminated food, especially seafood, is a key source of human perfluoroalkyl and polyfluoroalkyl substances (PFAS) exposure. Here, we examine the occurrence of PFAS in shiner surfperch (Cymatogaster aggregata), striped bass (Morone saxatilis), white croaker (Genyonemus lineatus), and seven other sport fish from San Francisco Bay, California, U.S. over a decade of monitoring to assess the potential risks from dietary exposures. In fish collected in 2009, 2014, and 2019, perfluorooctanesulfonic acid (PFOS) was predominantly detected at levels exceeding PFOS consumption advisory thresholds established in other U.S. states. The southern regions of San Francisco Bay have been especially impacted, with over 80% of samples above the strictest U.S. PFOS consumption advisory threshold set in Massachusetts (for one eight-ounce serving a week) at 3.5 ng/g (detected range: 2.0-18 ng/g ww) compared to only 8% in the other subembayments (detected range: 0.59-8.5 ng/g ww). An additional 19 PFAS were detected, with particularly elevated levels of 7:3 fluorotelomer carboxylic acid (7:3 FTCA) found in several species and sites, representing the first observations in marine fish globally. These findings indicate the need to consider a wider range of PFAS in assessing dietary exposure risks and environmental impacts.
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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 | 0.001 |
| 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 | 0.000 | 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".