An Advanced Group Contribution Method for High‐Dimensional, Sparse Data Sets
Bibliographic record
Abstract
Today's chemical processes involve many components, and it is necessary to know their basic physical properties for process design and operation. However, it is not always possible to find the property information of all components in the literature. Generally, there are two ways to evaluate properties of chemical compounds when they do not exist in the literature: the experimental measurement and predictive approaches based on empirical models. The latter is called the group contribution method (GCM), and its basic concept is that specific functional groups or fragments of a molecule contribute to the value of its physical property. The advantage of the GCMs is that they reduce the effort and cost compared to experiments. This study proposes a novel GCM method suitable for high-dimensional, sparse data sets. In order to improve its applicability and accuracy, the database is extended and divided into non-ring group compounds and ring group ones. Support vector regression (SVR) is adopted as the regression model, and a derivative-free optimization approach, referred to as particle swarm optimization, is incorporated into the parameter optimization step in learning the SVM model to avoid local optimality. Performance of the proposed model is compared to those of other GCMs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".