Characterization and Quantification of Mining-Related “Naphthenic Acids” in Groundwater near a Major Oil Sands Tailings Pond
Bibliographic record
Abstract
The high levels of acid extractable organics (AEOs) containing naphthenic acids (NAs) found in oil sands process-affected waters (OSPW) are a growing concern in monitoring studies of aquatic ecosystems in the Athabasca oil sands region. The complexity of these compounds has substantially hindered their accurate analysis and quantification. Using a recently developed technique which determines the intramolecular carbon isotope signature of AEOs generated by online pyrolysis (δ(13)Cpyr), natural abundance radiocarbon, and high resolution Orbitrap mass spectrometry analyses, we evaluated the sources of AEOs along a groundwater flow path from a major oil sands tailings pond to the Athabasca River. OSPW was characterized by a δ(13)Cpyr value of approximately -21‰ and relatively high proportions of O₂ and O₂S species classes. In contrast, AEO samples located furthest down-gradient from the tailings pond and from the Athabasca River were characterized by a δ(13)Cpyr value of around -29‰, a greater proportion of highly oxygenated and N-containing compound classes, and a significant component of nonfossil and, hence, non-bitumen-derived carbon. The groundwater concentrations of mining-related AEOs determined using a two end-member isotopic mass balance were between 1.6 and 9.3 mg/L lower than total AEO concentrations, implying that a less discriminating approach to quantification would have overestimated subsurface levels of OSPW. This research highlights the need for accurate characterization of "naphthenic acids" in order to quantify potential seepage from tailings ponds.
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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 | 0.000 | 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".