A Multiperiod Model for Assessing the Socioeconomic Impacts of Oil Spills during Arctic Shipping
Bibliographic record
Abstract
As the rate of ice melt in the Arctic increases, the potential for shipping activities is also increasing. However, infrastructure along the northwest passage (NWP) in Canada's Arctic is almost nonexistent. This presents major challenges to any response efforts in the case of a natural disaster. Also, the Arctic is home to many indigenous communities, as well as flora and fauna. Thus, it is of vital importance to protect the livelihood of the rights holders in this area and the Arctic marine environment. To do this, it is necessary to develop a decision-making tool to assess the potential risk of pollutants arising from increased shipping activity. Understanding such, this article assesses the impacts of a potential oil spill on communities in the Canadian Arctic. The consequences of risk are presented using a multiperiod model while the likelihood is analyzed using Bayesian Network. The output of the multiperiod model is incorporated into an influence diagram for risk assessment purposes. The Bayesian model benefits from expert elicitation from the crew aboard a research ship passing through the NWP. Information was also obtained from marine insurance companies, government representatives, and other Arctic specialized professionals. The risk-based model is subsequently applied to the Canadian Arctic area, with the aim of evaluating the impact of a potential oil spill through shipping.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".