Quantitative and Qualitative Analysis of Naphthenic Acids in Natural Waters Surrounding the Canadian Oil Sands Industry
Bibliographic record
Abstract
The Canadian oil sands industry stores toxic oil sands process-affected water (OSPW) in large tailings ponds adjacent to the Athabasca River or its tributaries, raising concerns over potential seepage. Naphthenic acids (NAs; C(n)H(2n-Z)O(2)) are toxic components of OSPW, but are also natural components of bitumen and regional groundwaters, and may enter surface waters through anthropogenic or natural sources. This study used a selective high-resolution mass spectrometry method to examine total NA concentrations and NA profiles in OSPW (n = 2), Athabasca River pore water (n = 6, representing groundwater contributions) and surface waters (n = 58) from the Lower Athabasca Region. NA concentrations in surface water (< 2-80.8 μg/L) were 100-fold lower than previously estimated. Principal components analysis (PCA) distinguished sample types based on NA profile, and correlations to water quality variables identified two sources of NAs: natural fatty acids, and bitumen-derived NAs. Analysis of NA data with water quality variables highlighted two tributaries to the Athabasca River-Beaver River and McLean Creek-as possibly receiving OSPW seepage. This study is the first comprehensive analysis of NA profiles in surface waters of the region, and demonstrates the need for highly selective analytical methods for source identification and in monitoring for potential effects of development on ambient water quality.
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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 | 0.001 |
| 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".