Quantitative Structure−Property Relationship (QSPR) Prediction of Liquid Viscosities of Pure Organic Compounds Employing Random Forest Regression
Bibliographic record
Abstract
A quantitative structure−property relationship (QSPR) approach was used to develop a predictive model for viscosities of pure organic liquids using a set of 403 compounds that belong to diverse classes of organic chemicals. A pool of 116 descriptors that encode topostructural, topochemical, electrotopological, geometrical, and quantum chemical properties of the organic compounds was used to develop QSPR models, based on the robust Random Forest (RF) regression algorithm. The performance of the algorithm, in terms of correlation coefficients and mean square errors, was determined to be good. The capability of the algorithm to build models and select the most-informative features simultaneously is very useful for several quantitative structure−activity/property relationship tasks. The eight most-dominant features selected by the RF regression algorithm primarily contained predictors that encode characteristics of atoms and groups that form hydrogen bonds, as well as factors involving molecular shape and size.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".