Using 2D and 3D Oil Spill Trajectory and Fate Models to Assess the Risk of Accidental Crude Oil Releases Along the Enbridge Line 3 Replacement Program Pipeline
Bibliographic record
Abstract
The proposed Enbridge Line 3 Replacement Program would replace the aging pipeline from Hardisty, Alberta, Canada to Superior, Wisconsin, USA. For the Canadian route, an Ecological and Human Health Risk Assessment (EHHRA) was prepared for the National Energy Board (NEB) in Canada. In the United States, an Assessment of Accidental Releases (AAR) and the Supplemental Release Report were part of an Environmental Impact Statement (EIS) prepared for the Minnesota Public Utilities Commission (PUC) and Minnesota Department of Commerce, Energy Environmental Review and Analysis (DOC-EERA). Computational oil spill modeling was used to assess the predicted trajectory (movement), fate (behavior and weathering), and potential effects (impacts) associated with accidental releases of crude oil along the proposed pipeline. This modeling included the 2-dimensional OILMAPLand and 3-dimensional SIMAP models. A total of 64 hypothetical release scenarios were investigated to understand the range of potential trajectories, fates, and effects that may be possible from multiple product types (Bakken, Federated Crude, and Cold Lake Winter Blend), released at any location, under varying environmental conditions. Trajectory and fate modeling was used to predict the downstream movement and timing of oil, as well as the expected surface oil thickness, water column contamination, shoreline and sediment oiling, and proportion evaporated to the atmosphere. These results were then used to assess the potential environmental effects to demonstrate the variability of outcomes following a release under different release conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".