Optical methods for rapid quantitative analysis of bitumen content in oil sands
Bibliographic record
Abstract
Proven oil reserves in Canada are estimated at 170 billion barrels, of which 160 billion barrels are oil sands located in Alberta. Oil sands are the remnants of degraded conventional oils mixed with large proportions of sand. The bitumen content in typical oil sands may vary from 1% to 18%. To reduce the energy and water consumption in the extraction process, one of the most important parameters for oil sands production is the bitumen content in the ore, but very few techniques are available for online monitoring of the bitumen content in oil sands. The objective of this study is to develop optical techniques for rapid monitoring of the bitumen content in oil sands. A high-speed optical scanner in combination with a telescope was built to measure the bitumen content in oil sands using the scattered light intensity and fluorescence signal from oil sands. The bitumen contents were determined with a good signal-to-noise ratio, and 2D bitumen content maps were obtained. Compared to commercial near infrared reflectance ore analyzers, this new method is insensitive to the water content and the background light intensity. The possibility of using Raman scattering for bitumen content measurements was also investigated. Although qualitative analysis was possible, quantitative analysis was difficult because of the relatively weak signal and spot-dependence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".