Future scenarios of commercial freight shipping in the Euro-Asian Arctic
Bibliographic record
Abstract
As climate warms and modern technologies advance, the Artic waters may offer new opportunities for shipping, notably in the Euro-Asian Arctic. This paper presents five alternative scenarios for commercial destination and transit shipping in the region until 2050. Using a pluralistic backcasting approach to foresight, these scenarios were co-created by the authors of this paper together with thirteen experts in relevant fields from seven different countries. The scenario-building exercise integrated global and regional factors and demonstrated that the future of commercial shipping in the Arctic is subject to vast uncertainties in global politics and global development trajectory alongside the sea ice conditions and technological progress. While the current volumes of commercial shipping in the Euro-Asian Arctic are insignificant, its future will largely depend on the development of these factors and how they will interface with each other. Plausible futures of commercial shipping in the region range from extensive international transit shipping through the Northern Sea Route to restricted shipping by vessels with Arctic flags only or even no shipping, to shipping over the transpolar route. The scenarios presented here can be used to inform national policymaking as well as to support strategic decision-making within corporate entities operating in related industries.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".