The Effects of Diluted Bitumen on Developing Fathead Minnow (Pimephales Promelas)
Bibliographic record
Abstract
Canada has experienced a significant increase in the transport of diluted bitumen (dilbit), a predominant oil sands product that is created by combining bitumen with diluents derived from oil-gas condensates and other proprietary compounds. The chemical composition of dilbit varies to meet season and transport conditions. While the toxicological effects of a variety of crude and refined oils are well studied, the toxicity of dilbit to fish embryos, which are immobile and thus at a high a risk of exposure to oil in the event of a spill remain largely unknown. To fill this gap, this study assessed the toxicity of two Winter dilbit blends, Access Western Blend (AWB) and Cold Lake Blend (CLB), to fathead minnow (Pimephales promelas) embryos. Embryos were exposed to a range of water accommodated fractions (WAF) and chemically enhanced WAF (CEWAF; using Corexit®9500A as dispersant) for both AWB and CLB. After exposure, hatched fish were assessed for blue sac disease (BSD) and expression of selected biomarker genes. Developmental malformations were observed more frequently with increasing concentrations of AWB and CLB in both WAF and CEWAF treatments and included tube heart, pericardial edema, and craniofacial deformities. Levels of cyp1a and gst mRNA were elevated with increasing total concentrations of polycyclic aromatic hydrocarbons in both AWB and CLB WAF and CEWAF conditions. However, there were no significant changes in mRNA levels for p53, sod, cat, and gsr. These results suggest that expression of cyp1a and gst may serve as biomarkers for dilbit exposure in fathead minnow, furthering our understanding of dilbit-responsive indicators of toxicity in fish species native to North America. This study is important as it utilizes the same exposure methodology to examine the toxicity of two commonly used Canadian dilbits, facilitating the comparison of dilbit toxicity.
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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".