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Record W2315532603 · doi:10.5539/jsd.v9n2p127

The Sustainable Relationship between Navigation of Arctic Passages, Arctic Resources, and the Environment

2016· article· en· W2315532603 on OpenAlexvenueno aff
Chunjuan Wang, Dahai Liu, Meng Qiao Xu, Ying Yu, Xiaoxuan Li, Junguo Gao, Wenxiu Xing

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsArcticArctic ecologySustainabilityResource (disambiguation)The arcticClimate changeGlobal warmingEnvironmental scienceSustainable developmentEnvironmental resource managementGeographyOceanographyEcologyComputer scienceGeology

Abstract

fetched live from OpenAlex

The Arctic passages are economically important waterways connecting North America, Western Europe, and East Asia. Global climate change is melting the Arctic sea ice and will improve the navigability of the Arctic passages. The opening of the Arctic route will facilitate the exploration and development of Arctic resources, which can alleviate the energy crisis, but it may ignite a worldwide “Arctic resource war”. Besides, the navigation of the Arctic passages will also burden the Arctic eco-environment and trigger a wave of eco-environmental effects in the Arctic region include exacerbating environmental pollution, threatening survival of life, intensifying climate change, changing local production and lives, even worldwide. On this basis, this study uses a system dynamics analysis of positive and negative effects to review the resource and environmental effects due to opening the Arctic passages and proposes the measures to ensure the resource and environmental sustainability.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.260
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2016
Admission routes1
Has abstractyes

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