Comparative evaluation of three high-performance liquid chromatography–based <i>K</i>ow estimation methods for highly hydrophobic organic compounds: Polybrominated diphenyl ethers and hexabromocyclododecane
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Bibliographic record
Abstract
Three methods for estimating the octanol-water partition coefficient (Kow) based on its relationship with capacity factors on reversed-phase (RP) high-performance liquid chromatography (HPLC) columns were compared in terms of their applicability to highly hydrophobic compounds (HHCs). Methods based on simple isocratic elutions were found to be unsuitable, because the very high organic modifier fractions that are required to elute HHCs from RP columns challenge the basic assumption of the similarity between the octanol-water and RP-eluent systems. Compound planarity was found to exert a considerable influence on the retention of HHCs in RP columns, leading to different linear calibration curves for chlorobenzenes and chlorobiphenyls. Only an empirical exponential regression succeeded in describing the behavior of both groups of compounds during gradient elutions. In a method based on isocratic retention times at multiple temperatures, satisfactory calibration was achieved with a multivariate linear regression that included a numerical indicator of compound planarity. Considering experimental simplicity, speed, precision, and accuracy, with the latter judged by comparison with Kow values for polybrominated diphenyl ethers and polychlorinated naphthalenes as reported in the literature, a gradient elution combined with an exponential calibration curve is recommended for estimating the Kow of HHCs. To our knowledge, the first isomer-specific Kow values for hexabromocyclododecane are reported. Bearing in mind that the influence of structural characteristics on retention is likely to increase with hydrophobicity, it is not justified to judge a HPLC-based Kow estimation method as being suitable for HHCs because it is shown to work well for less hydrophobic substances. Whereas univariate linear regressions often may prove to be sufficient when dealing with substances having a log Kow, of less than five, methods for HHCs need to account for the influence of structure on retention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it