Congener specific organic carbon normalized soil and sediment-water partitioning coefficients for the C~1~ through C~8~ perfluoroalkyl carboxylic and sulfonic acids
Bibliographic record
Abstract
Abstract Organic carbon normalized soil and sediment-water partitioning coefficients (K~oc~) were estimated for all C~1~ through C~8~ perfluoroalkyl carboxylic (PFCA) and sulfonic (PFSA) acid congeners. The limited experimental K~oc~ dataset for the straight chain C~7~ through C~10~ PFCAs and C~8~ and C~10~ PFSAs was correlated to SPARC and ALOGPS computationally estimated octanol-water partitioning / distribution constants and used to predict K~oc~ values for both branched and linear C~1~ through C~8~ isomers. Branched and linear congeners in this homologue range are generally expected to have K~oc~ values >1, leading to their accumulation in organic matter on sediments and soils, retardation during ground and pore water flow, and the preferential association with dissolved organic matter in aquatic systems. Both increasing perfluoroalkyl chain length and linearity increase K~oc~ values with substantial intra- and inter-homologue variation and interhomologue mixing. Variability in K~oc~ values among the PFCA and PFSA congeners will likely lead to an enrichment of more linear and longer chain isomers in organic matter fractions, resulting in aqueous phases fractionated towards shorter chain branched congeners. The expected magnitude of fractionation will require inclusion in source apportionment models and risk assessments. A comparison of representative established quantitative structure property relationships for estimating K~oc~ values from octanol-water partitioning constants suggests that these equilibrium partitioning frameworks may be applicable towards modeling PFCA and PFSA environmental fate processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".