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Record W2557434553

Six seas: comparative application of investment attractiveness methodology to Arctic offshore petroleum privinces

2016· dissertation· en· W2557434553 on OpenAlexaboutno aff
Rodion Kravchenko

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessSubmarine pipelinePetroleumArcticInvestment (military)OceanographyThe arcticPetroleum engineeringEngineeringEnvironmental scienceMarine engineeringPolitical scienceGeologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

In the 2000s, Arctic energy sources development climbed to the top of the energy companies' and authorities' agendas.The necessity to increase production volumes and the desire to find new large oil and gas fields triggered investment flows into the Arctic.However, in 2014 the situation changed.The sudden oil price decline left most of the Arctic resources economically unviable and upended the strategic priorities of the main market players: booming investment was replaced by austerity measures.Previously it had been expected that the Arctic states would compete over the natural resources, but with low energy prices they started to compete for investment instead as only the most economically attractive projects could now be financed.This raises the question of the comparative attractiveness of different parts of the Arctic, as oil and gas companies choose where to invest their money.This thesis therefore develops a methodology for analyzing and comparing the investment attractiveness of Arctic offshore petroleum provinces and applies it to nine Arctic maritime areas spread across six Arctic seas.The nine areas are selected, assessed and compared on four dimensions that can affect their attractiveness for investment in oil and gas projects: climatic harshness, geography, petroleum taxation system and quality of investor protection.Based on this multidimensional analysis the Arctic Seas are ranked according to their investment attractiveness.The most attractive areas are the Russian Barents and Kara Seas and the Alaskan Chukchi Sea.Thereafter comes the Norwegian Barents Sea, followed by the Canadian Beaufort Sea, and then Canada's Baffin Bay.The lowest ranked are the Laptev Sea, the Alaskan Beaufort Sea and Greenland's Baffin Bay.The analysis represents quite a general and, to a certain extent, simplified approach and is intended only as a first step in the complex process of investment decision-making.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.087
GPT teacher head0.399
Teacher spread0.311 · 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 designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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