Identifying the causes of oil sands coke leachate toxicity to aquatic invertebrates
Bibliographic record
Abstract
A previous study found that coke leachates (CL) collected from oil sands field sites were acutely toxic to Ceriodaphnia dubia; however, the cause of toxicity was not known. Therefore, the purpose of this study was to generate CL in the laboratory to evaluate the toxicity response of C. dubia and perform chronic toxicity identification evaluation (TIE) tests to identify the causes of CL toxicity. Coke was subjected to a 15-d batch leaching process at pH 5.5 and 9.5. Leachates were filtered on day 15 and used for chemical and toxicological characterization. The 7-d median lethal concentration (LC50) was 6.3 and 28.7% (v/v) for pH 5.5 and 9.5 CLs, respectively. Trace element characterization of the CLs showed Ni and V levels to be well above their respective 7-d LC50s for C. dubia. Addition of ethylenediaminetetraacetic acid significantly (p ≤ 0.05) improved survival and reproduction in pH 5.5 CL, but not in pH 9.5 CL. Cationic and anionic resins removed toxicity of pH 5.5 CL only. Conversely, the toxicity of pH 9.5 CL was completely removed with an anion resin alone, suggesting that the pH 9.5 CL contained metals that formed oxyanions. Toxicity reappeared when Ni and V were added back to anion resin-treated CLs. The TIE results combined with the trace element chemistry suggest that both Ni and V are the cause of toxicity in pH 5.5 CL, whereas V appears to be the primary cause of toxicity in pH 9.5 CL. Environmental monitoring and risk assessments should therefore focus on the fate and toxicity of metals, especially Ni and V, in coke-amended oil sands reclamation landscapes.
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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 | 0.000 |
| 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".