Distribution of Saturates, Aromatics, Resins, and Asphaltenes Fractions in the Bituminous Layer of Athabasca Oil Sands
Bibliographic record
Abstract
The composition and distribution of saturates, aromatics, resins, and asphaltenes (SARA) fractions in the bituminous layer on the surface of Athabasca oil sands were identified using elemental analysis (EA), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (SEM) with an energy-dispersive spectrometer (EDS), and Fourier transform infrared spectrometry (FTIR). The contents of elements sulfur (S) and nitrogen (N) and the ratios of carbon/sulfur (C/S) and carbon/nitrogen (C/N) were characterized as potential indicators for evaluating the distribution of SARA fractions in the bituminous layer. Results indicated that saturates and aromatics tend to deposit at the outer bituminous layer, while asphaltenes and resins were inclined to distribute at the inner layer. Results also suggested that the distribution of SARA fractions was thermodynamically dependent and susceptible to thermal treatment. On the basis of the experimental results, a conceptual distribution model was proposed, which is supposed to serve as a basis for future studies on the liberation of bitumen from oil sands and the operation conditions for oil sands processing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".