Guidelines to Prepare for Oil Sands Product Spills in Varied Aquatic Environments
Bibliographic record
Abstract
ABSTRACT On July 24, 2007, the Westridge Transfer Line in Burnaby, British Columbia, ruptured spilling 1,400 barrels of oil sands product into the area's storm water systems and eventually into the Burrard Inlet at Vancouver Harbor. The response to this spill was considered successful and there is no record of oil sinking. Several years later, in July of 2010, the Line 6B pipeline operated by Enbridge Energy Partners LLP ruptured spilling 20,082 barrels of oil sands product into the Kalamazoo River. In contrast to the Burnaby spill, this response was extremely difficult due to the sinking of large quantities of oil. The variance in fate and behavior of the oil sands products in these two spills demonstrates how environmental factors can result in different response challenges. Many environmental factors affect the fate of spilled oil sands products in aquatic environments because bitumen, a large component of oil sands products, has a density greater than freshwater. By analyzing specific factors in areas at risk, responders can better prepare for, and expect, submergence in oil sands product spills. Areas identified to have low salinity, rough sedimentation, high turbidity, strong sunlight exposure, high temperatures, and strong currents have a high risk of submergence. Response teams in these areas of high risk should have submerged oil recovery equipment readily available for rapid deployment.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".