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Record W3110110773 · doi:10.1051/e3sconf/202020802003

Reducing the discrepancy between regional specific electricity consumption as a way to increase the structural stability of the Russian economy

2020· article· en· W3110110773 on OpenAlexaboutno aff
С. А. Некрасов

Bibliographic record

VenueE3S Web of Conferences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic shortagePer capitaConsumption (sociology)ElectricityEconomicsProduction (economics)Redistribution (election)AgricultureEconomyBusinessAgricultural economicsGeographyMacroeconomicsPopulationPolitical science

Abstract

fetched live from OpenAlex

In 2010-2020 the dynamics and absolute values of regional per capita electric power consumption (EPC) in the European part of Russia was comparable with similar indicators in Western European countries, and in the Asian part - with the countries of Northern Europe, the USA and Canada. If all over the world there is a reduction in the differentiation in energy consumption between developing and developed countries, then in Russia in 1990-2012 this indicator increased, which reflects the ongoing decline in the structural stability of the domestic economy. The change in this negative trend in 2012-20018 shown on the tools of the theory of technocenoses. The need to concentrate efforts not on the growth of the EPC in regions with developed production and primary redistribution of natural resources, but on reducing the divergence of regions in terms of the value of the EPC is substantiated. In regions with low EPC, the problem is not a shortage of electricity, but the weak development of industrial and agricultural production.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.925

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.291
Teacher spread0.220 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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