A Critical Review of a Recommended Analytical and Classification Approach for Organic Fluorinated Compounds with an Emphasis on Per- and Polyfluoroalkyl Substances
Bibliographic record
Abstract
Abstract Organic fluorinated compounds have been detected in various environmental media and biota. Some of these compounds are regulated locally (e.g., perfluorononanoic acid maximum contaminant level in drinking water by the New Jersey Dept. of Environmental Protection), nationally (e.g., perfluorooctanoic acid maximum acceptable concentration in drinking water by Health Canada), or internationally (e.g., Stockholm Convention on Persistent Organic Pollutants). Globally, regulators and researchers seek to identify the organic fluorinated compounds associated with potential adverse effects, bioaccumulation, mobility, and persistence to manage their risks, and, to understand the beneficial attributes they bring to products such as first responder gear, etc. Clarity is needed to determine the best analytical method for the goal of the analyses (e.g., pure research or analysis to determine the extent of an accidental release, monitoring groundwater for specific compounds to determine regulatory compliance, and establish baseline levels in a river of organic fluorinated substances associated with human health risk prior to a clean-up effort). Analytical techniques that identify organic fluorine coupled together with targeted chemical analysis will yield information sufficient to identify public health or environmental hazards. Integr Environ Assess Manag 2021;17:331–351. © 2020. W.L. Gore & Associates Inc. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS This paper provides new and important clarity on fluorine-containing compounds, their uses, toxicological, chemical, and physical properties in relation to environmental occurrence, analysis, and how the interpretation of the analytical data is critical to effective regulation. To effectively protect public health and the environment, efforts must focus on those fluorinated compounds that pose the greatest risk, which necessitates an understanding of exposure pathways and determination of the concentration of the hazardous substance therein. Since national and internationally accepted standards do not always exist, this paper addresses analytical approaches so that fluorinated organic compounds can be identified and quantified appropriately.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".