Viscosity Mixing Rule and Viscosity–Temperature\nRelationship Estimation for Oil Sand Bitumen Vacuum Residue and Fractions
Bibliographic record
Abstract
Removing\nheavy components through solvent separation is a potential\nroutine for the viscosity reduction of Canadian oil sand bitumen.\nThe mixing rule for the viscosity of extracted fraction is the basis\nof process simulation and optimization. In this study, supercritical\nfluid extraction fractionation was applied to separate Canadian oil\nsand bitumen vacuum residue (VR) into various fractions. The viscosity\nblending behavior of extracted fractions was experimentally evaluated.\nSeveral available viscosity mixing rules were tested. To predict the\nviscosity–temperature profile of derived fractions at different\nblending ratio, we proposed a new mixing rule based on empirical equation\nparameters. We also correlated the viscosity–temperature parameters\nto conventional bulk property, providing a method for the rapid viscosity\nestimation for VR, extracted fractions, and their mixtures.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".