Quantitative Structure−Property Relationship (QSPR) Models for Boiling Points, Specific Gravities, and Refraction Indices of Hydrocarbons
Bibliographic record
Abstract
The relationship between physical properties (normal boiling point, specific gravity, and refractive index) of hydrocarbons in the distillate boiling range and their molecular structures was examined using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. Multiple linear regression equations with up to eight parameters were used to develop predictive multilinear regression models. Six models for boiling point, specific gravity, and refractive index were obtained for the saturate and aromatic compounds separately. The correlation coefficients ( R 2 ) for all six models were >0.99, except for the model of specific gravity for aromatics, which had a correlation coefficient of R 2 = 0.9881. The standard deviations over the 186-point saturate training set were 6.10 K, 0.007, and 0.004 for boiling point, specific gravity, and refractive index, respectively. Those over the 200-point aromatic training set were 6.30 K, 0.008, and 0.005, respectively. Leave-one-out cross-validation (CV) checks that were performed for all the obtained models led to only slightly smaller correlation coefficients ( R 2 − < 0.002). Another set of 34 hydrocarbon compounds was chosen as the validation set to test the saturate models. Aromatic validation sets were also chosen to test the accuracy of the models for boiling point (61 data points), specific gravity (36 data points), and refractive index (27 data points). All the models showed excellent performance, with average errors of <1%. A separate set of three models was obtained for the combined saturates and aromatics data set. Compared to the models obtained for saturates and aromatics separately, these three more-general models were less accurate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".