Equilibrium modeling: A pathway to understanding observed perfluorocarboxylic and perfluorosulfonic acid behavior
Bibliographic record
Abstract
Equilibrium distribution models of hydrophobic neutral partitioning of the perfluorinated carboxylic and sulfonic acids were shown, without the need for any physical chemical properties, to successfully predict the sediment-water distribution (D(SW) ) directly from independently measured equilibrium tissue distributions known as the bioconcentration factor (BCF). The constant of proportionality required by the models successfully predicted the correlation between the biotic and abiotic distributions of both sets of chemicals, thus demonstrating the applicability of the assumptions inherent in the models, that is, hydrophobically driven partitioning of the neutral species, and thus the applicability of the models themselves. Colloquially speaking, the models are thus validated as applicable to these chemicals. Subsequent application of the standard equilibrium models showed order of magnitude agreement for 83% of measured BCF values and 88% of measured D(SW) for the perfluorinated carboxylic acids, confirming the physical chemical properties used. The applicability of the models to perfluorooctane sulfonic acid (PFOSA) was shown by the successful prediction of D(SW) from BCF. Therefore, the measured D(SW) and BCF could be used to calculate the octanol-water distribution, D(OW) , and hence the corresponding pK(a):K(OW) solution set, thus providing independent experimentally based estimates of these properties. For both the perfluorinated carboxylic and sulfonic acids, the existing standard equilibrium models are shown to be applicable.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".