Chemometric Fingerprinting of Petroleum Hydrocarbons Within Oil Sands Tailings Using Comprehensive Two-Dimensional Gas Chromatography
Bibliographic record
Abstract
Base Mine Lake (BML) is the first full-scale demonstration of water-capped tailing technology in a pit lake to reclaim lands impacted by surface mining in the Alberta Oil Sands Region (AOSR). Biogeochemical cycling and/or exchange near the fluid water interface (FWI) of the organic-rich fluid fine tailings (FFT) can hinder the reclamation process. To monitor this activity, sedimentary depth profiles were collected from three platforms (P1 to P3) at BML. Seventy-four chromatographically well-resolved petroleum hydrocarbon (PHC) isomers were quantified at each depth interval using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC/TOFMS). The range of total concentrations of all isomers examined across the FFT was the highest at P1 (range = 3.6 × 100–5.5 × 103 ng/g TOC), second highest at P2 (range = 3.8 × 100–1.9 × 103 ng/g TOC), and lowest at P3 (range = 5.6 × 100–7.1 × 102 ng/g TOC). The elevated levels of the same isomers across platforms suggest a consistent source fingerprint. While the source fingerprint was mostly consistent across the platforms and depths, Principal Component Analysis (PCA) identified small differences between geospatial locations caused by variations in specific isomer concentrations. Hierarchical Clustering Analysis (HCA) identified the isomers responsible for the PCA separation, showing that the concentrations of low-molecular-weight n-alkanes (C11–C13) and drimane varied compared to the heavier PHCs with depth. These alkanes are the most biodegradable of the compounds identified in this study, and their variations may reflect biogeochemical cycling within the FFT. Combining these statistical tools provided deeper insight into how isomer concentrations vary with depth, helping to identify possible influences like changing inputs, biogeochemical cycling, and species exchange with the water column.
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 | 0.002 | 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) | 0.000 | 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 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".