Solubility correlation by model with partial molar volume
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Jouyban‐Acree/van't Hoff model was often used to correlate solubility data, which was related to temperature, and the mole fraction of each solvent in the mixture of two solvents. In this work, the partial molar volume of each component in the mixture of two solvents was first time introduced as a modification to the Jouyban‐Acree/van't Hoff model. Furthermore, machine learning and artificial intelligence (AI) technology based on temperature, mole fraction, and partial molar volume of each solvent were employed to improve the accuracy of the solubility estimation. The models were evaluated in terms of their ability to mathematically correlate solute solubility in binary solvents. An average root mean square deviation (RMSD) is used to measure the deviation between the calculated values and the experimental values. With partial molar volume as a modified parameter of the Jouyban‐Acree/van't Hoff model, the overall RMSD of all the correlations of solubility improved.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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