Characterization of Athabasca lean oil sands and mixed surficial materials: Comparison of capillary electrophoresis/low‐resolution mass spectrometry and high‐resolution mass spectrometry
Bibliographic record
Abstract
RATIONALE: Oil sands mining in Alberta, Canada, requires removal and stockpiling of considerable volumes of near-surface overburden material. This overburden includes lean oil sands (LOS) which cannot be processed economically but contain sparingly soluble petroleum hydrocarbons and naphthenic acids, which can leach into environmental waters. In order to measure and track the leaching of dissolved constituents and distinguish industrially derived organics from naturally occurring organics in local waters, practical methods were developed for characterizing multiple sources of contaminated water leakage. METHODS: Capillary electrophoresis/positive-ion electrospray ionization low-resolution time-of-flight mass spectrometry (CE/LRMS), high-resolution negative-ion electrospray ionization Orbitrap mass spectrometry (HRMS) and conventional gas chromatography/flame ionization detection (GC/FID) were used to characterize porewater samples collected from within Athabasca LOS and mixed surficial materials. GC/FID was used to measure total petroleum hydrocarbon and HRMS was used to measure total naphthenic acid fraction components (NAFCs). HRMS and CE/LRMS were used to characterize samples according to source. RESULTS: The amounts of total petroleum hydrocarbon in each sample as measured by GC/FID ranged from 0.1 to 15.1 mg/L while the amounts of NAFCs as measured by HRMS ranged from 5.3 to 82.3 mg/L. Factors analysis (FA) on HRMS data visually demonstrated clustering according to sample source and was correlated to molecular formula. LRMS coupled to capillary electrophoresis separation (CE/LRMS) provides important information on NAFC isomers by adding analyte migration time data to m/z and peak intensity. CONCLUSIONS: is a possible tracer for LOS seepage. CE/LRMS provides complementary information and is a feasible and practical option for source evaluation of NAFCs in water.
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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 | 0.001 |
| 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 | 0.000 |
| 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".