Monitoring Bitumen Upgrading, Bitumen Recovery, and Characterization of Core Extracts by Hydrocarbon Group-type SARA Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Hydrocarbon group-type analyses are presented in this work for characterization of samples related to the Province of Alberta's abundant bitumen reserves that demand more effective ways of utilization and valorization. The main objective of the study is the evaluation of thin layer chromatography with flame ionization detection (TLC-FID) for rapid hydrocarbon group-type monitoring of bitumen samples and determination of the feasibility of each method for application with very small sample amounts. TLC-FID is a known technique for hydrocarbon (HC) group-type analysis. Different methods of utilization of TLC-FID are assessed here. The analytical techniques employed and data obtained are presented and compared. HC group types are presented in term of saturates, aromatics, resins, and asphaltenes (SARA). Applications are shown for three types of studies related to bitumen: with respect to ultradispersed catalytic bitumen upgrading; solvent-based enhanced bitumen production (Vapex); and characterization of organic extracts from reservoir cores. Techniques are evaluated and validated for each utilization and the preferred methodology indicated.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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