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Determination of the Average Specific Capital Consumptions of CCPP with Gas Turbine of 30-125 MW and Put into Operation at TPPs of the Russian Federation from 2015 to the First Quarter of 2021

2021· article· en· W3216153467 on OpenAlexaboutno aff
Е. Л. Степанова, Svetlana Sushko, А. П. Овчинников

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGas turbinesRussian federationElectricityInvestment (military)Quarter (Canadian coin)Combined cycleCapital investmentEnvironmental scienceTurbineElectricity generationCapital costElectric powerEngineeringOperations managementBusinessPower (physics)Electrical engineeringFinanceMechanical engineeringPolitical sciencePhysicsEconomic policyGeography

Abstract

fetched live from OpenAlex

Abstract Research was carried out on the volume of gas turbine commissioning in the range of unit capacities of 30–125 MW at TPPs of the Russian Federation operating as part of a CCPP in the period from 2015 to the first quarter of 2021. The purpose of the research was to determine the average specific capital investment in construction and the average specific fuel consumption for the supply of electricity and heat for the CCPP introduced over the years in the united energy systems of the Russian Federation. Gas turbines are classified by electrical capacity into three groups: from 100 to 125 MW, from 60 to 99 MW and from 30 to 59 MW. An assessment of the quantitative distribution of gas turbines over the interconnected energy systems of the Russian Federation has been carried out. The results of the comparison of the quantitative commissioning of gas turbines are shown in the period from 2010 to the economic crisis of 2014 and in the period from 2015 to the present. A preliminary estimate of the increase in average specific capital investments in the construction of CCPP, which included gas turbines of the same electric power, was made for these periods.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.246

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designBench or experimental
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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