Development of CAMD based on the hybrid gene algorithm and simulated annealing algorithm and the application on solvent selection
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, an evolutionary approach, improved CAMD based on the hybrid gene algorithm and simulated annealing algorithm (GASA), is developed. The new approach combines the feature of GA and SA, avoiding the problem of prematurity. With a new category strategy of candidate groups from the Mod UNIFAC group database adopted, a repair operator is introduced to guarantee the integrity of randomly generated molecules and thus the search of straight chain alkane as well as cyclane solutions can be performed together. The properties of molecules are obtained by the group contribution method. An example of extractive solvent selection for a methanol‐methyl acetate system has been illustrated in detail to further explain the method. The stability as well as other properties of the molecules which the authors think important is considered in the fitness function. Results of the example show that the fitness ranking values are better than those in the literature. The CAMD method in this paper can be used in practical chemical processes.
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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