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Record W4394748316 · doi:10.31857/s0869587323030052

Territorial shifts in anthropogenic pressure on the environment in post-Soviet Russia

2023· article· en· W4394748316 on OpenAlexaboutno aff
N. N. Klyuev

Bibliographic record

VenueВестник Российской академии наук · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationEnvironmental protectionGeographyMineral resource classificationPopulation pressureQuarter (Canadian coin)Environmental changePhysical geographyEnvironmental scienceOceanographyClimate changePopulation growthGeologyArchaeology

Abstract

fetched live from OpenAlex

The changes in production and population in Russian regions for the years 1990–2020 are considered as an indirect indicator of the changes in the total anthropogenic pressure on the environment. It is shown that the pressure increases primarily in well-developed territories and decreases in vast underdeveloped areas. A new environmentally unfavorable trend in environmental pressures is the relative shift to coastal regions, i.e., to the vulnerable and recreationally attractive coasts of the Atlantic seas as well as the Caspian Sea. Simultaneously, there is a clear northeastern vector in the development of the mining industry, which leads to the formation of new local centers of large-scale impacts on ecologically significant and vulnerable landscapes of East Siberia, the Far East, the European North, and the shelf zones. In the 21st century, most of the main mining regions have increased their extraction of mineral resources: half of them by 50% or more and a quarter by 200% or more. It is shown that the environmental situation is more likely to deteriorate further in the regions with a strong increase in anthropogenic pressure than it is to improve in the regions that are now leaders in terms of reducing adverse impacts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.307
Teacher spread0.281 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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