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Cross-polar transit potential of Russia: what prevents its implementation?

2021· article· en· W3185448335 on OpenAlexaboutno aff
Yu N Gladkiy, В Д Сухоруков, K Yu Eidemiller, A B Almazova-Ilyina

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsPolitical scienceInternational tradeBusinessPolitics

Abstract

fetched live from OpenAlex

Abstract Attention is drawn to the cross-polar transit potential of Russia in connection with the expansion of integration ties between the US and Canada, on the one hand, and the countries of East, South-East and South Asia, on the other. It is emphasized that the maintenance of cross-polar routes is considered a product of “high redistribution” and requires significant capital investments. In the Russian literature, the problem of economic efficiency of the organization of transcommunication messages has become the object of research relatively recently. It was the subject of fundamental disagreements among the authors, as some of them are of the opinion that for Russia the operation of Polar routes brings little commercial benefit and poorly takes into account the geopolitical interests of the country. One of the main reasons for this was the transition of the world’s airlines to the use of new aircraft models, and, accordingly, non-stop cross-polar flights. At the same time, the hopes of the authorities of certain Siberian regions to “get rich” at the receptions of thousands of flights a month were “put a cross”. The hypothetical possibility of Russia closing cross-polar air routes in response to the policy of economic sanctions of Western countries is discussed. It is concluded that such a step is unacceptable due to the hypothetical closure of the sky by EU countries for Russian carriers. The authors are convinced that in any case, the Russian Federation needs to take radical measures to develop its own polar aviation, build new modern airports in Siberia, as well as to improve air navigation services for flights in the harsh conditions of 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 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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.017
GPT teacher head0.295
Teacher spread0.277 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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