Impacts of Oil Spills on Arctic Marine Ecosystems: A Quantitative and Probabilistic Risk Assessment Perspective
Bibliographic record
Abstract
Oil spills resulting from maritime accidents pose a poorly understood risk to the Arctic environment. We propose a novel probabilistic method to quantitatively assess these risks. Our method accounts for spatiotemporally varying population distributions, the spreading of oil, and seasonally varying species-specific exposure potential and sensitivity to oil. It quantifies risk with explicit uncertainty estimates, enables one to compare risks over large geographic areas, and produces information on a meaningful scale for decision-making. We demonstrate the method by assessing the short-term risks oil spills pose to polar bears, ringed seals, and walrus in the Kara Sea, the western part of the Northern Sea Route. The risks differ considerably between species, spatial locations, and seasons. Our results support current aspirations to ban heavy fuel oil in the Arctic but show that we should not underestimate the risks of lighter oils either, as these oils can pollute larger areas than heavier ones. Our results also highlight the importance of spatially explicit season-specific oil spill risk assessment in the Arctic and that environmental variability and the lack of data are a major source of uncertainty related to the oil spill impacts.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".