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Record W2198324667 · doi:10.3968/7745

The Fastest Growing Fossil Fuel Source in the European Union

2015· article· en· W2198324667 on OpenAlexvenueno aff
Zhichao Zhang

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsNatural gasEuropean unionFossil fuelNatural resource economicsConsumption (sociology)IndigenousPipeline transportEnergy demandInternational tradeEconomicsEconomyEnvironmental scienceEngineeringEcologyWaste management

Abstract

fetched live from OpenAlex

Energy forecasts predict natural gas to be the fastest growing fossil fuel source in the next 2-3 decades in Europe. However, projections of European gas demand are being revised downwards. Due to decrease in indigenous production and increase in natural gas primary consumption certainty about future gas demand in the European Union has changed over the past few years. Thus is expected to become a more dependent to import needed capacity from outside of Europe satisfying the European increasing demand for natural gas and at the same time protecting the climate and environment is one of the greatest challenges of this age. This paper demonstrates the main important planned natural gas pipeline’s mechanisms for Europe to see how much of expected future demand of Europe will be covered by planned natural gas pipelines. If there will be no economical and most important political, etc issues it seems planned pipelines will be operated in the time scheduled and cover the most part of European demand raised by decrease in indigenous and increase in primary consumption in coming decades.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.003

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 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
Published2015
Admission routes1
Has abstractyes

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