High-resolution mass spectrometry (HRMS) methods for nontarget discovery and characterization of poly- and per-fluoroalkyl substances (PFASs) in environmental and human samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it