High-resolution mass spectrometry (HRMS) methods for nontarget discovery and characterization of poly- and per-fluoroalkyl substances (PFASs) in environmental and human samples
Bibliographic record
Abstract
Widespread environmental contamination of legacy long-chain poly- and per-fluoroalkyl substances (PFASs) has triggered chemical regulatory action and a global transitioning to alternative PFASs. More than 5000 PFASs are now recognized on various lists, but few have been monitored despite ample evidence of unidentified organic fluorine in human and environmental samples. Nevertheless, our review of the literature indicates that nontarget analytical methods based on high-resolution mass spectrometry have been used to discover more than 750 PFASs, belonging to more than 130 diverse classes, in strategically selected environmental samples, biofluids or commercial products. Among these reports, we summarize the analytical and data-processing strategies for nontarget PFAS discovery, identify knowledge gaps and propose new areas for method development. Discovery of emerging PFASs before they are global contaminants could mitigate future contamination if strategic techniques can be developed to prioritize some of these substances for synthesis and confirmation, further monitoring, source elucidation and hazard characterization.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".