Policy and governance simulation for Arctic marine routes
Bibliographic record
Abstract
This paper introduces PoGo (Policy and Governance), a simulation framework designed to assess the operational impacts of maritime policies on shipping, particularly for navigation in sea ice. By integrating ship performance models, metocean conditions, local community factors, regulations like the Polar Code and Carbon Intensity Indicator, and employing a route optimization algorithm, PoGo enables the evaluation of policy decisions and their effects on rights-holders and ship operators. The main elements of PoGo are described and the framework of the code is outlined. A case study on a cargo transportation service to Iqaluit is used to exemplify some the tool’s features, focusing on vessel ice class, transportation season, fuel and crewing costs, carbon emissions, and voyage time. This application highlights how PoGo can be used as a strategic tool, identifying consequences of policy changes, navigation restrictions, and future regulation revisions. Effectively, the simulation framework can be used to assess the sensitivity of policy decisions in the context of Arctic shipping, and balance economic and other considerations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".