Diluted bitumen weathered under warm or cold temperatures is equally toxic to freshwater fish
Bibliographic record
Abstract
Canada is one of the main petroleum producers in the world. Through its oil sands exploitation, a viscous bitumen mixed with sand, water, and clay is being produced. This bitumen is so viscous that approximatively 20%–30% of diluent needs to be added to ease transportation, resulting in a mixture called diluted bitumen (dilbit). The transport of dilbit through North America comes with a potential risk for oil spills in freshwater ecosystems at any time of the year. In this study, a mesoscale spill tank was used to study dilbit spills in freshwater to understand the effect of cold (winter-like) vs. warmer (spring- and fall-like) water temperatures on its natural weathering and their toxicity to fathead minnow (Pimephales promelas) embryos. Water samples were collected weekly during two consecutive 35-day experiments ran at either 2 or 15 °C. Each week, fish larvae were exposed for 7 days, and water analysis was performed. Chemical analysis showed that the volatile organic compound, total organic carbon, and polycyclic aromatic hydrocarbon concentrations decreased in both experiments with time, while fish larvae exposed to both temperature settings yielded increased abnormalities, EROD activity, CYP1A, and glutathione S-transferase mRNA expression levels, and decreased heart rate. Importantly, there were no major differences between the temperature regimes on dilbit weathering, highlighting that if a spill occurs in colder waters, it would be equally toxic to organisms. This work provides new data on the potential risk of oil spill for use during response planning and modelling.
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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) | 0.001 | 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".