Surrogate chromatographic models and the solvation parameter model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it