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Ecological Insurance of Arctic Oil and Gas Producing Shelf Projects in Russia

2023· article· en· W4319343580 on OpenAlexaboutno aff
O. V. Kudryavtseva, E. V. Serebrennikov

Bibliographic record

VenueVestnik of the Plekhanov Russian University of Economics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsUnderwritingArcticLegislationOil spillThe arcticBusinessEnvironmental scienceEnvironmental resource managementEnvironmental protectionOceanographyFinanceGeologyPolitical science

Abstract

fetched live from OpenAlex

The article deals with problems of ecological insurance of arctic oil and gas producing shelf projects in Russia and possible ways of its development. The authors put forward the method of estimating financial security of operations on eliminating possible ecological damage in case of hydrocarbon spill on arctic shelf oil and gas producing fields and study oil spills on arctic shelf fields ‘Prirazlomnoye’ and ‘Pobeda’. They estimate the cost of measures aimed at elimination of such incidents and propose a special indicator, i.e. financial loss factor for oil spill. Empiric conclusions obtained by comparative analysis of legislation in the field of ecology of entrails use in Russia, Norway, the US and Canada can be used for optimization of the effective Russian legislation and introduction of obligatory ecological insurance. Data of mathematic spill modeling can be applied by insurance companies for calculating insurance premium, by Rosprirodnadzor – for charging penalties and for other supervision bodies – for underwriting oil and gas arctic projects.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.171
Teacher spread0.160 · 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
Published2023
Admission routes1
Has abstractyes

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