Isotopic Analyses Fingerprint Sources of Polycyclic Aromatic Compound-Bearing Dust in Athabasca Oil Sands Region Snowpack
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Fugitive dust associated with surface mining activities is one of the principal vectors for transport of airborne contaminants in Canada’s Athabasca oil sands region (AOSR). Effective environmental management requires quantitative identification of the sources of this dust. Using natural abundance radiocarbon (Δ 14 C) and dual (δ 13 C, δ 2 H) compound-specific isotope analysis (CSIA), this study investigated the sources of dust and particulate-bound polycyclic aromatic compounds (PACs) deposited in AOSR lake snowpack. Lower Δ 14 C values, higher particulate and PAC loadings, and lower δ 13 C values for phenanthrene and C1-alkylated phenanthrenes/anthracenes (C1-Phen) at sites closer to the mining operations indicated unprocessed oil sand and/or petroleum coke (petcoke–a byproduct of bitumen upgrading) as major sources of anthropogenic fugitive dust. However, a Bayesian isotopic mixing model that incorporated both δ 13 C and δ 2 H could discriminate petcoke from oil sand, and determined that petcoke comprised between 44 and 95% (95% credibility intervals) of a C1-Phen isomer at lakes <25 km from the heart of the mining operations, making it by far the most abundant source. This study is the first to demonstrate the potential of CSIA to provide accurate PAC source apportionment in snowpack and reveals that petcoke rather than oil sand is the main source of mining-related particulate PACs deposited directly to AOSR lakes.
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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 | 0.000 |
| 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".