Capillary Electrophoresis-Mass Spectrometry Characterization of Water Samples Derived from Athabasca Lean Oil Sands and Mixed Surficial Materials
Bibliographic record
Abstract
The Athabasca oil sands in Alberta, Canada are one of the world’s largest reserves of petroleum. Lean oil sands (LOS) overburden is removed and stockpiled prior to surface mining results in vast amounts of waste materials. Seepage of potentially toxic substances such as naphthenic acid fraction compounds (NAFCs) from LOS is an environmental and human health concern. We report the first use of a capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) to characterize NAFCs in water samples derived from Athabasca LOS and mixed surficial materials. The results are compared with conventional flow-injection negative-ion ESI Orbitrap high resolution MS of the same sample set. The CE-ESI-MS methods explored: separation of underivatized NAFC with negative ion mode MS, separation of derivatized NAFC with positive ion mode MS, and the use of pH gradients and additives. The mass resolution of the Orbitrap MS was set to 240,000 (m/z 250) and full-scan mass spectra were acquired over m/z range 100-600. All formula assignments were < 2 ppm mass error allowing for accurate profiling and class distribution and DBE determination. All samples contained a preponderance of petroleum hydrocarbons (0.7 – 18.3 mg/L) in the carbon number range of C7-30. The levels of NAFCs were in the range 5-85 mg/L. The versatility of CE-MS is shown to be (i) well suited for environmental forensics and (ii) a complementary tool for separation and characterization of lean oil sands and mixed surficial materials. Preliminary evidence is presented for use of OxSyheteroatomic species as tracers of possible seepage from the LOS materials. Presented at Pacifichem 2015, Honolulu, HI, USA
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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 | 0.000 | 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 | 0.001 | 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".