Solubility correlation by model with partial molar volume
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".