A CORRELATION FOR VISCOSITY AND SOLVENT MASS FRACTION OF BITUMEN-DILUENT MIXTURES
Bibliographic record
Abstract
A simple equation is presented for predicting the kinematic viscosity of bitumens and heavy oils mixed with diluents. The correlation has been shown to provide accurate viscosity estimates of these mixtures for a wide range of data and requires only the knowledge of the pure bitumen and pure solvent viscosities at any given temperature. The correlation makes use of a viscosity reduction parameter which reduces error significantly when compared to similar equations presented by Chirinos et al. (1983), and Cragoe (1933). For a total of 89 data points, excluding the pure bitumens and diluents values, the correlation yielded an overall average absolute deviation of about 14 percent. The same equation was applied to predict the mass fraction of diluent required to reduce bitumen viscosity to pumping viscosity. Predicted values matched experimental values very well, with an overall average absolute deviation of about 6 percent.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".