Feature analysis of generic <scp>AI</scp> models for <scp>CO<sub>2</sub></scp> equilibrium solubility into amines systems
Bibliographic record
Abstract
Abstract Reported models have disadvantages such as poor prediction accuracy and time‐consuming. And they can not reflect the impact of chemical reactions on CO 2 solubility. To compensate for these deficiencies, parameters representing operational parameters, physical properties, chemical properties, and molecular properties are introduced as input variables. A series of models are constructed by three algorithms: back propagation neural network, radial basis function neural network, and random forest. The model with the best prediction performance is level OPCM (RBFNN), with the AARE of only 1.52%. By ranking the importance of the features using the RF algorithm, P CO2 , was found to be the key parameter affecting the CO 2 loadings, with M being the least important. Using the screened key parameters to model the model, as well as optimizing the structure, can further improve the predictive performance of the model. The full process development and optimization model framework constructed in this article can provide practical guidance for the development of machine learning models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".