Meteorological environment and risks along the Arctic Northeast Passage
Bibliographic record
Abstract
With rapid warming in the Arctic region, the navigability of the Arctic has improved in recent decades. Except for the sea ice, meteorological conditions are also very important for navigation safety. In this study, crucial meteorological factors and their control areas along the Northeast Passage (NEP) were analyzed by using observations. In addition, the risks in the operations and safety of sailors were investigated with meteorological thresholds identified by the Inuit people. The impact area of low temperatures is the East Siberian Sea (−13 °C to −17 °C under Shared Socioeconomic Pathway 1-2.6), especially in the waters located north of the New Siberian Islands and within the Dmitrii Laptev Strait and Sannikov Strait. The precipitation, wind speed, and visibility decrease, and the atmospheric humidity increases eastward from the Kara Sea. The Kara Sea and Laptev Sea are mainly dominated by eastward wind, while westward wind is very significant in the East Siberian Sea and Chukchi Sea. Under the Shared Socioeconomic Pathway 2-4.5 scenario, the Kara Sea is projected to present low meteorological risk from 2026–2030, while higher risks persist in the eastern NEP. In contrast, under the Shared Socioeconomic Pathway 5-8.5 scenario, medium risks become more prevalent, particularly through the Sannikov Strait and north of the New Siberian Islands. The above results are highly important for the meteorological navigation of Arctic shipping and for the formulation of relevant international navigation policies in the Arctic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".