Differentiation of two industrial oil sands process‐affected waters by two‐dimensional gas chromatography/mass spectrometry of diamondoid acid profiles
Bibliographic record
Abstract
RATIONALE: Processing of the oil sands of Canada has produced large amounts of process-affected water (OSPW). Concerns have been raised over the possible environmental impacts of any leakage of OSPW from storage lagoons which contain toxicants, including organic acids. Natural weathering of oil sands deposits may also produce the toxicants, including the acids. Therefore, there is a need for differentiation of the possible natural and industrial sources of such toxicants and also for methods suitable for monitoring changes in the composition of OSPW during long-term storage. METHODS: Here we show in a simple preliminary study of the two samples currently available to us, by use of comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GCxGC/ToF-MS), the distributions of methyl esters of individual isomeric diamondoid acids in OSPW from lagoons with different histories and from different industrial operators. RESULTS: We show that the distributions of methyl esters of individual isomeric diamondoid acids, including methyladamantane carboxylic and ethanoic acids, identified by comparison with data for reference compounds, can be differentiated readily. The use of acids with known structures, each verified by authentic acids, known toxicities and known and/or predictable physicochemical properties, to distinguish the different sources is advantageous, since factors likely to control the fate and dispersion of the acids can then more easily be predicted. It is postulated that the differences observed in the relative amounts of some of the acids result from variable extents of bacterial transformation of the organic matter in OSPW. CONCLUSIONS: The differences in distributions of diamondoid acids clearly vary between the two samples of OSPW and may prove very useful for monitoring the fate of different sources of OSPW both in storage and in the wider environment, once a wider collection of representative samples is available for study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".