An assessment of organic solvent based equilibrium partitioning methods for predicting the bioconcentration behavior of perfluorinated sulfonic acids, carboxylic acids, and sulfonamides
Bibliographic record
Abstract
Abstract SPARC, KOWWIN, and ALOGPS octanol-water partitioning (log K~ow~) and distribution (log D) constants were calculated for all C~1~ through C~8~ and the straight chain C~9~ through C~15~ perfluoroalkyl sulfonic acids (PFSAs) and carboxylic acids (PFCAs). Application of five established models for estimating bioconcentration factors (BCFs) were applied to the PFSA and PFCA log K~ow~ and log D data and compared to available field and laboratory BCF data. Wide variability was observed between the methods for estimating log K~ow~ and log D values, ranging up to several log units for particular congeners, and which was further compounded by additional variability introduced by the different BCF equations applied. With the exception of n-perfluorooctanecarboxylic acid (n-PFOA), whose experimental BCF was poorly modeled by all approaches, the experimental BCF values of the other PFSA and PFCA congeners were reasonably approximated by the ALOGPS log P values in combination with any of the five log K~ow~ based BCF equations. The SPARC and KOWWIN log K~ow~ and log D values provided generally less accurate BCF estimates regardless of the BCF equation applied. However, the SPARC K~ow~ values did provide BCF estimates for PFSA congeners with errors <0.3 log units using any of the five BCF equations. Model lipophilic and proteinophilic solvent based distribution constant calculations for the PFSA and PFCA congeners with experimental BCFs exhibited similar relationships with their corresponding BCF values. For longer chain PFCA and PFSA congeners, increasing hydrophobicity of the perfluoroalkyl chain appears to be driving corresponding increases in BCF values. Perfluorooalkyl sulfonamides are expected to display similar chain length and branching pattern influences on BCFs, but no experimental data are currently available upon which to validate the estimated values which range widely between the various approaches by up to 10 log units. The amidic proton acidity on primary and secondary perfluoroalkyl sulfonamides will play a significant role in the partitioning of these compounds with both abiotic and biotic organic matter, and will need to be taken into account when assessing their environmental and biological fate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".