Arctic shipping 2013–2022: the traffic has grown, with big variation between regions, seasons and ship types
Bibliographic record
Abstract
This article analyses decadal changes in Arctic ship traffic from 2013 to 2022, using data from the Arctic Ship Traffic Data system (ASTD). Shipping in waters affected by sea ice has grown, but how much depends on geographical definitions. The Polar Code area had an average annual growth of 8.7%, mainly due to more traffic in the Barents Sea, where most Arctic ship traffic occurs. Where analysts set the southern boundary of the Barents Sea significantly influences the statistics, for example, to what extent fishing vessels dominate Arctic shipping. Reports on Arctic shipping should consider the significant intra-Arctic variations in activity levels, growth rates and traffic composition. The Kara Sea experienced the biggest annual growth rate—14% on average—because of petroleum projects that have introduced big oil and gas tankers. In contrast, there is minimal activity and growth in the Large Marine Ecosystems of the Northern Canadian Archipelago and the Central Arctic Ocean. Even though the winter traffic has grown in the Barents Sea, the Kara Sea and Baffin Bay, the activities there remain distinctly seasonal. In other seas, ships almost vanish in winter. Transit shipping over the Arctic is still insignificant in a global context. The standard reports in the ASTD are important for understanding Arctic shipping and should be improved. In particular, the Polar Code area needs to be subdivided to enable consistent reporting on overall pan-Arctic and intra-Arctic developments. Definitions for transit traffic should also be agreed upon and opportunities for automatic reporting accordingly investigated.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".