Freshwater Sediment Toxicity Evaluation from Meso-Scale Spill Tests of Diluted Bitumen and Conventional Crude
Bibliographic record
Abstract
Oil and gas development and transportation in many areas of the world, such as the oil sands region of Alberta, Canada, are heavily monitored to minimize the environmental impacts of development and the risk of oil spills. However, oil spills to aquatic environments still occur. Although the science of oil spills has received considerable attention of late, uncertainty still remains in evaluating the fate and transport of oil spills as well as the effects of those spills on aquatic biota. Experiments using meso-scale spill tanks were used to examine the physical and chemical behavior of two types of oil, conventional crude (CC) and diluted bitumen (DB), under similar environmentally relevant scenarios (i.e., volume of spill, temperature, duration, wave action, and presence of river sediment). In addition, biological impact assessments via sediment toxicity testing collected from the oil spill tests were conducted. Sediments were evaluated for acute toxicity using three standard sediment test species: Hyalella azteca, Lumbriculus variegatus, and Chironomus dilutus. Sediments collected from the CC simulated spill showed a higher level of acute toxicity than sediments collected from spills with DB. Higher toxicity in the CC-contaminated sediment was supported by higher concentrations of low molecular weight polycyclic aromatic hydrocarbons (PAHs) when compared with the DB-contaminated sediment, while the remaining PAH profile was similar between the contaminated sediments. The use of a meso-scale spill tank in combination with sediment bioassays allowed for the evaluation of oil spills under controlled and environmentally relevant conditions (e.g., nearshore high sediment loading river), and in turn provides assessors with additional information to identify the appropriate mitigation and remediation efforts needed in the event of future spills. Environ Toxicol Chem 2022;41:2797-2807. © 2022 SETAC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.035 | 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 teacher head, 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".