Comparing simulated shallow subsurface spills of diluted bitumen and conventional crude oil
Bibliographic record
Abstract
Increased oil production in Canada has resulted in proposals to extend or develop new oil pipelines. Many of these proposals have been met by concerns from the public over potential environmental impacts related to construction and the potential for oil spills to negatively affect groundwater quality. Crude oil sourced from the Alberta oil sands represents a significant proportion of this increase in production. This crude oil is produced as bitumen, which is subsequently diluted with light hydrocarbons to lower viscosity to allow for pipeline transport producing diluted bitumen. In this study, we pumped water through tanks filled with sand to simulate groundwater flow. Tanks were injected with either conventional crude or diluted bitumen to simulate a crude oil spill from a pipeline rupture occurring below the water table representing a pipeline river crossing scenario. Water samples were collected from the downstream end of the tanks throughout the experiment period (∼two months). Compared to water quality guidelines, effluent waters from both conventional crude and diluted bitumen tanks contained elevated concentrations of dissolved organic compounds, particularly benzene, ethylbenzene, toluene and xylenes (BTEX). The effluent from each tank had similar concentrations of benzene, whereas discharge water from conventional crude tanks contained higher concentrations of ethylbenzene, toluene and xylenes. In both tanks, and as expected, the BTEX concentrations appeared to be proportional to those determined in their injected crude oils. The measured dissolved concentrations of benzene, ethylbenzene and toluene are lower than predicted which is attributed largely due to dilution along the flow path. In addition to organic constituents, effluent sampled from the diluted bitumen tank contained some metals (Co, Cr, Fe and V) which measured constituents of the oil.
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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) | 0.001 | 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 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".