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Scientific approaches to defining the territorial boundaries of the Arctic

2019· article· en· W2964602686 on OpenAlexaboutno aff
V P Federov, Valery Zhuravel, Sergey Grinyaev, Dmitriy A. Medvedev

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticTreatySubarctic climateSovereigntyThe arcticPermafrostGeographyMaritime boundaryPhysical geographyPoliticsArctic ecologyBoundary (topology)OceanographyPolitical scienceClimatologyGeologyLawInternational law

Abstract

fetched live from OpenAlex

Abstract The article deals with the problem of determining the boundaries of the Arctic territories belonging to the Arctic. The authors identified political, economic and other factors affecting the definition of territories belonging to the Arctic. Documents of subarctic countries, in particular, Canada, USA, Norway, etc., are considered. It is noted that in the Arctic countries there is no universal understanding of the territories belonging to the Arctic and the Arctic zone. It is especially difficult to determine the southern borders of the Arctic. Also there is no international agreement or treaty that unambiguously and unequivocally define the legal status of the Artic. In USSR main criteria for identifying the southern boundary of the Arctic were the Arctic Circle, the mean multiyear 10ºC isotherm of July, and the permafrost zone. However, the use of variables leads to the “mobility” of the Arctic boundaries, especially in the face of climate change on the planet. On the basis of Russian domestic documents, the boundaries of the territories that are included in the Arctic zone of the Russian Federation are determined. The authors draw attention to the need for Russia to protect its sovereignty in the Arctic, the elimination and correction of errors in this direction.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.014
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.036
GPT teacher head0.234
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations25
Published2019
Admission routes1
Has abstractyes

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