Assessment of risk for aromatic hydrocarbons resulting from subsea Blowouts: A case study in eastern Canada
Bibliographic record
Abstract
• Representative met-ocean patterns of Newfoundland offshore waters are yielded. • Novel risk assessment, data mining, and stochastic simulation is integrated. • PAHs-related risk is more significant than VOC hazard in Newfoundland waters. • Injecting subsea dispersant may be inadequate to tackle oil blowouts. There is increasing concern over the environmental risks associated with deepwater petroleum exploration activities. The integration of environmental risk assessment and oil spill modeling can help to understand and quantitatively characterize the potential risks from subsea blowouts in specific regions. This study integrates a novel deepwater oil spill model (DWOSM) and an extended stochastic modeling methodology to assess the environmental risk of polycyclic aromatic hydrocarbons (PAHs) during a simulated offshore subsurface blowout off the east coast of Newfoundland, Canada. Additionally, the effectiveness of subsea dispersant injection (SSDI) in spill mitigation was investigated through comparative simulations. Resultant spill hazard and risk maps for current and proposed areas of offshore oil and gas development, in support of contingency plans, revealed that surfaced oil tends to drift toward the southeast and east in the hypothetical blowout case; nearshore areas of east Newfoundland have relatively low risk shortly after a deep-sea blowout; released PAHs may elicit more adverse ecological impacts than volatile organic compounds (VOCs); and SSDI application can reduce contaminant exposure levels but at the expense of enlarging the impacted zone for a short term. This stochastic simulation-based risk assessment provides scientific evidence to support decision-making in strategic oil spill response operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".