Diamonds in the Rough: Identification of Individual Naphthenic Acids in Oil Sands Process Water
Bibliographic record
Abstract
Expansion of the oil sands industry of Canada has seen a concomitant increase in the amount of process water produced and stored in large lagoons known as tailings ponds. Concerns have been raised, particularly about the toxic complex mixtures of water-soluble naphthenic acids (NA) in the process water. To date, no individual NA have been identified, despite numerous attempts, and while the toxicity of broad classes of acids is of interest, toxicity is often structure-specific, so identification of individual acids may also be very important. Here we describe the chromatographic resolution and mass spectral identification of some individual NA from oil sands process water. We conclude that the presence of tricyclic diamondoid acids, never before even considered as NA, suggests an unprecedented degree of biodegradation of some of the oil in the oil sands. The identifications reported should now be followed by quantitative studies, and these used to direct toxicity assays of relevant NA and the method used to identify further NA to establish which, or whether all NA, are toxic. The two-dimensional comprehensive gas chromatography-mass spectrometry method described may also be important for helping to better focus reclamation/remediation strategies for NA as well as in facilitating the identification of the sources of NA in contaminated surface waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".