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Record W4410635219 · doi:10.1016/j.rcar.2025.05.009

Meteorological environment and risks along the Arctic Northeast Passage

2025· article· en· W4410635219 on OpenAlexaboutno aff
Jinlei Chen, Shichang Kang, Hulin Sun

Bibliographic record

VenueResearch in Cold and Arid Regions · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersScience and Technology Program of Gansu ProvinceNational University of Defense TechnologyNational Natural Science Foundation of ChinaState Key Laboratory of Cryospheric ScienceChina Association for Science and Technology
KeywordsArcticThe arcticEnvironmental sciencePhysical geographyOceanographyGeographyClimatologyGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.412
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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