A comparative assessment of octanol-water partitioning and distribution constant estimation methods for perfluoroalkyl carboxylates and sulfonates
Bibliographic record
Abstract
Abstract New experimental data is available in the literature regarding the octanol-water distribution behavior of representative straight chain perfluoroalkyl carboxylate (PFCA) and sulfonate (PFSA) congeners. The current study provides the first investigation into the predictive ability of various software programs for estimating the corresponding octanol-water partitioning (log P) and distribution (log D) constants of PFCAs and PFSAs. Wide predictive variation was found within and between the various methods. Several programs were able to accurately estimate the log P/D fragmental contributions of a -CF~2~- group for PFCAs, as well as the associated Gibbs free energies for partitioning into octanol from water due to the hydrophobic character of the perfluoroalkyl chain (Δ~hydrophobic~G~ow~). Only the SPARC log D method accurately predicted the electrostatic contributions of the carboxylate head group (Δ~electrostatic~G~ow~) towards octanol-water partitioning for PFCAs. Similar log D values and organic carbon normalized sediment-water partitioning coefficients (K~oc~) for PFCAs and PFSAs having equivalent perfluoroalkyl chain lengths suggests potentially equivalent Δ~electrostatic~G~ow~ and Δ~hydrophobic~G~ow~ contributions towards lipophilic partitioning for these two contaminant classes at near neutral pH values, regardless of head group identity. In contrast, there are potentially different Δ~electrostatic~G~ow~ and Δ~hydrophobic~G~ow~ contributions towards proteinophilic partitioning under biologically relevant conditions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".