Determination of Solute Descriptors for the Solvation Parameter Model by Reversed‐Phase Liquid Chromatography Binary and Ternary Solvent Systems: A Case Study
Bibliographic record
Abstract
The solvation parameter model is a versatile tool validated across various applications for characterizing chromatographic systems and estimating biophysical and environmental properties of interest. The general form of the solvation parameter model for applications where transfer of neutral compounds between two condensed phases uses five solute descriptors, where, except for McGowan's characteristic volume (V), the remaining four descriptors are determined using a combination of reversed-liquid chromatography, gas chromatography, and liquid-liquid partitioning experimental data. In this study, we investigated the determination of solute descriptors for the solvation parameter model using binary and ternary solvent systems in reversed-phase liquid chromatography on a single stationary phase. As a proof of concept, we replicate the descriptor values found in the WSU descriptor database with those of solute descriptors determined using reversed-phase liquid chromatography binary and ternary solvent systems on a single stationary phase for 31 compounds. As a case study, we demonstrated the application of this approach by determining solute descriptors for 13 new compounds using this proposed approach. Standard error values for the estimated descriptors ranged from 0.019 for 1-fluoro-4-nitrobenzene to 0.080 for 4-methylbenzaldehyde.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".