Effects-Directed Analysis of Dissolved Organic Compounds in Oil Sands Process-Affected Water
Bibliographic record
Abstract
Acute toxicity of oil sands process-affected water (OSPW) is caused by its complex mixture of bitumen-derived organics, but the specific chemical classes that are most toxic have not been demonstrated. Here, effects-directed analysis was used to determine the most acutely toxic chemical classes in OSPW collected from the world's first oil sands end-pit lake. Three sequential rounds of fractionation, chemical analysis (ultrahigh resolution mass spectrometry), and acute toxicity testing (96 h fathead minnow embryo lethality and 15 min Microtox bioassay) were conducted. Following primary fractionation, toxicity was primarily attributable to the neutral extractable fraction (F1-NE), containing 27% of original organics mass. In secondary fractionation, F1-NE was subfractionated by alkaline water washing, and toxicity was primarily isolated to the ionizable fraction (F2-NE2), containing 18.5% of the original organic mass. In the final round, chromatographic subfractionation of F2-NE2 resulted in two toxic fractions, with the most potent (F3-NE2a, 11% of original organic mass) containing predominantly naphthenic acids (O2(-)). The less-toxic fraction (F3-NE2b, 8% of original organic mass) contained predominantly nonacid species (O(+), O2(+), SO(+), NO(+)). Evidence supports naphthenic acids as among the most acutely toxic chemical classes in OSPW, but nonacidic species also contribute to acute toxicity of OSPW.
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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".