Nontarget Mass Spectrometry Reveals New Perfluoroalkyl Substances in Fish from the Yangtze River and Tangxun Lake, China
Bibliographic record
Abstract
Nontarget high-resolution mass spectrometry (Nt-HRMS) has been proven useful for the identification of unknown poly- and perfluoroalkyl substances (PFASs) in commercial products and water, but applications to biological samples are limited. China is the major PFAS-manufacturing nation; thus, here, we adapted our Nt-HRMS methods to fish collected from the Yangtze River and Tangxun Lake to discover potentially bioaccumulative PFASs in aquatic organisms destined for human consumption. In addition to traditional PFASs, over 330 other fluorinated analytes belonging to 10 classes of PFASs were detected among the pooled fish livers, including 6 sulfonate classes, 2 amine classes, 1 carboxylate class, and 1 N-heterocycle class. One class was detected in samples from both locations, 8 classes were detected exclusively in Tangxun Lake fish, and 1 class was detected exclusively in Yangtze River fish, 10 km downstream of a fluorochemical manufacturing site where we first reported these substances in wastewater 3 years ago. Overall, 4 of the PFAS classes (>165 analytes) are reported for the first time here. Wider monitoring and toxicological testing should be a priority for understanding the health risks posed to people and wildlife exposed to these substances.
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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 | 0.001 |
| 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".