Prediction of the McAllister Model Parameters by Using the Group-Contribution Method: <i>n</i>-Alkane Liquid Systems
Bibliographic record
Abstract
The McAllister model is considered to be the best correlating technique for viscosity−composition data. In a series of publications, Asfour et al. and Nhaesi and Asfour successfully converted the McAllister model into a predictive model which requires only the viscosities of the pure components and the molecular parameters of the constituents of a liquid mixture. They validated the model for the cases of n -alkane and regular liquid systems by using data on a large number of liquid mixtures at different temperatures. In this paper, we propose a novel technique for predicting the McAllister model parameters, for n -alkane systems, by the group-contribution method. The predictive capability of the McAllister model in this case is shown, for n -alkane binary and multicomponent systems, to be better or at least as good as the techniques reported earlier. The main advantage of the technique we are proposing here is that we expect it to be able to successfully and reliably predict more classes of liquid solutions than earlier methods.
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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 | 0.001 | 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 | 0.001 | 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".