A Database of Experimentally Derived and Estimated Octanol–Air Partition Ratios (<i>K</i>OA)
Bibliographic record
Abstract
Equilibrium partition coefficients or partition ratios are a fundamental concept in physical chemistry, with wide applications in environmental chemistry. While comprehensive data compilations for the octanol–water partition ratio and the Henry’s law constant have existed for many years, no comparable effort for the octanol–air partition ratio (KOA) exists. Considering the increasing use of KOA in understanding a chemical’s partitioning between a wide variety of organic phases (organic phases in atmospheric particles, plant foliage, polymeric sorbents, soil organic matter, animal tissues, etc.) and the gas phase, we have compiled all KOA values reported in the published literature. The dataset includes more than 2500 experimentally derived values and more than 10 000 estimated values for KOA, in total covering over 1500 distinct molecules. The range of measured log10 KOA values extends from −2 to 13. Many more measured values have been reported in the log10 KOA range from 2 to 5 and from 6 to 11 compared to the range from 5 to 6, which is due to the complementary applicability range of static and dynamic measurement techniques. The compilation also identifies measured data that are judged not reliable. KOA values for substances capable of undergoing strong hydrogen bonding derived from regressions with retention times on nonpolar gas chromatographic columns deviate strongly from values estimated by prediction techniques that account for such intermolecular interactions and should be considered suspect. It is hoped that the database will serve as a source for locating existing KOA data and for the calibration and evaluation of new KOA prediction techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".