Heterocyclic Aromatics in Petroleum Coke, Snow, Lake Sediments, and Air Samples from the Athabasca Oil Sands Region
Bibliographic record
Abstract
The aromatic fractions of snow, lake sediment, and air samples collected during 2011-2014 in the Athabasca oil sands region were analyzed using two-dimensional gas chromatography following a nontargeted approach. Commonly monitored aromatics (parent and alkylated-polycyclic aromatic hydrocarbons and dibenzothiophenes) were excluded from the analysis, focusing mainly on other heterocyclic aromatics. The unknowns detected were classified into isomeric groups and tentatively identified using mass spectral libraries. Relative concentrations of heterocyclic aromatics were estimated and were found to decrease with distance from a reference site near the center of the developments and with increasing depth of sediments. The same heterocyclic aromatics identified in snow, lake sediments, and air were observed in extracts of delayed petroleum coke, with similar distributions. This suggests that petroleum coke particles are a potential source of heterocyclic aromatics to the local environment, but other oil sands sources must also be considered. Although the signals of these heterocyclic aromatics diminished with distance, some were detected at large distances (>100 km) in snow and surface lake sediments, suggesting that the impact of industry can extend >50 km. The list of heterocyclic aromatics and the mass spectral library generated in this study can be used for future source apportionment studies.
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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) | 0.000 | 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".