The Effect of Volume Changes Due to Mixing on Diffusion CoefficientDetermination in Heavy Oil and Hydrocarbon Solvent System
Classification
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".
Why this work is in the frame
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Bibliographic record
Abstract
The diffusion coefficient is a key parameter to describe promising solvent - based technologies for heavy oil and bitumen recovery. In this work, an extended diffusion model considering the volume changes on mixing was used to calculate the mutual diffusion coefficients for the heavy oil -hydrocarbon solvent system. The approach requires not only concentration profiles, but also data on volume changes in the process of mixing. The concentration profiles were obtained from diffusion laboratory experiments in which X-ray Computer Assisted Tomography (CAT) was used to measure the density distributions at different times. In addition, the volume changes data was obtained by performing heavy oil and hydrocarbon solvent mixing experiments at different content of solvent in heavy oil.By comparing the diffusion coefficient versus concentration curves based on the extended diffusion model and on the usual diffusion model (1), it was concluded that the volume changes due to mixing make significant differences to the diffusion coefficient determination in heavy oil – hydrocarbon solvent system, and the mutual diffusion coefficients are strong functions of concentration, which is consistent with previous literature.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it