Surrogate chromatographic models and the solvation parameter model
Bibliographic record
Abstract
• Solvation parameter model provides a link between chromatographic and partition processes. • A visualization technique and d -parameter combination is a practical approach to identify surrogate chromatography systems. • Surrogate chromatographic systems are a cost effective approach in estimating partition properties of interest. Many partition processes of interest have been characterised using Abraham's solvation parameter model. However, to estimate physicochemical, environmental, or biophysical properties of compounds requires reliable solute descriptors. Exploitation of the correlation of partition processes and chromatographic retention data is an alternative approach to estimate properties of interest without relying on solute descriptors. The system constants of the solvation parameter model, along with screening tools such as Cos θ, d -parameter, D -parameter, principal component analysis, and hierarchical cluster analysis, facilitate the identification of similarities between partitioning and chromatographic systems. In this review we discuss various screening tools to identify similarities between partitioning and chromatographic systems as well as applications of surrogate chromatographic models to estimate physicochemical, environmental, and biophysical properties of interest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".