Predicting the Congener-Specific Environmental Behaviour of Perfluorinated Acid Contaminants Using Semi-Empirical Computational Methods
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
Abstract Perfluorinated acids (PFAs) are contaminants detected worldwide in a range of abiotic and biotic environmental matrices. The two major classes of PFAs include the perfluorinated carboxylic acids (PFCAs) and perfluorinated sulfonic acids (PFSAs), both of which are considered persistent and potentially bioaccumulative. Current research and regulatory efforts are focussed on the straight-chain members of each PFA class and homologue group, primarily because these congeners are the major components of technical mixtures and are also available as pure standards. However, the numerous potential branched congeners in each PFA class represent a poorly understood family of environmental contaminants whose environmental and toxicological properties may be more important than the more prevalent straight-chain members. To help broaden the current understanding of PFA environmental fate and toxicology, semi-empirical computational methods were used predict fundamental physico-chemical properties of all potential C4 to C8 PCFA and PFSA congeners. Established quantitative structure-activity models for other multi-class emerging and legacy contaminants were applied to estimate key parameters related to the toxicology, environmental partitioning, and abiotic and biotic degradation mechanisms for each PFA class. The findings provide guidance for developing new analytical methods for separating and identifying PFAs in environmental and technical mixtures, prioritizing efforts on synthesizing authentic standards, and focussing toxicological studies on the congeners most likely to be of concern.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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