Chemical Fingerprints of Alberta Oil Sands and Related Petroleum Products
Why this work is in the frame
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Bibliographic record
Abstract
Alberta oil sands are known to contain the world's largest reserves of bitumen. The rapid growth in their production could result in a significant environmental impact. Fingerprinting bitumen and petroleum products from the Alberta oil sands is essential in order to better understand the chemical compositions of oil sands, prepare for potential oil spills, and address the associated environmental problems. This study presents an integrated quantitative chemical characterization of Alberta oil sands bitumen and other related Alberta oils using gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The characterized target hydrocarbons include n-alkanes, unsubstituted polycyclic aromatic hydrocarbons (PAHs) and their alkylated homologues (APAHs), biomarker terpanes and steranes, bicyclic sesquiterpanes, and diamondoids. The chemical features of bitumen in oil sands are clearly distinguishable from those of most other conventional crude oils. The chemical fingerprints of diluted oil sands bitumen and Albian Heavy Synthetic crude were significantly altered by either the diluent blended with the former or the upgrading processing of crude bitumen in the latter. A chromatographic hump of unresolved complex mixtures (UCMs) eluting between n-C10 to n-C40 is pronounced and n-alkanes are nearly absent in bitumen extracted from oil sands. Alkylated naphthalenes account for only a small proportion of the total APAHs in Alberta oil sands extracts. The PAH compounds in oil sands extracts and diluted bitumen are dominated by alkylated homologues with the relative distribution of C0– < C1– < C2– < C3– for all five APAH series. Biomarker terpanes and cage-like adamantanes were determined in almost identical abundance and distribution profile in oil sands extracts and diluted crude bitumen, while biomarker steranes and bicyclic sesquiterpanes were removed to varying degrees by physical weathering or biodegradation.
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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 | 0.000 | 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) | 0.001 | 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 it