Distinguishing Natural from Anthropogenic Sources of Acid Extractable Organics in Groundwater near Oil Sands Tailings Ponds
Bibliographic record
Abstract
Abstract Distinguishing between naphthenic acids (NAs) associated with oil sands process-affected water (OSPW) and those found naturally in groundwaters in contact with the bituminous McMurray Formation poses a considerable analytical challenge to environmental research in Canada’s oil sands region. Previous work addressing this problem combined high-resolution Orbitrap mass spectrometry with carbon isotope values generated by online pyrolysis (δ13Cpyr) to characterize and quantify the acid extractable organics (AEOs) fraction containing NAs in the subsurface near an oil sands tailings pond. Here, we build upon this work through further development and application of these techniques at two different study sites near two different tailings ponds, in conjunction with the use of an additional isotopic tool─sulfur isotope analysis (δ34S) of AEOs. The combined use of both δ13Cpyr and δ34S allowed for discrimination of AEOs into the three end-members relevant to ascertaining the NA environmental footprint within the region: (1) OSPW; (2) McMurray Formation groundwater (i.e., naturally occurring bitumen), and; (3) naturally occurring non-bitumen. A Bayesian isotopic mixing model was used to determine the relative proportions of these three sources in groundwater at both study sites. Although background levels of OSPW-derived AEOs were generally low, one sample containing 49–99% (95% credibility interval) OSPW-derived AEOs was detected within an inferred preferential flow-path, highlighting the potential for this technique to track tailings pond seepage.
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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 | 0.000 | 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".