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Record W2946166901 · doi:10.3390/su11102988

Assessment of Photovoltaic Potential of Mining Sites in Uzbekistan

2019· article· en· W2946166901 on OpenAlexaboutno aff
Mokhinabonu Mardonova, Yosoon Choi

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersNational Research Foundation of Korea
KeywordsPhotovoltaic systemEnvironmental scienceGreenhouse gasRenewable energyPayback periodEnvironmental engineeringElectricityNet present valueSolar energyNatural gasMeteorologyEngineeringProduction (economics)Waste managementGeography

Abstract

fetched live from OpenAlex

The present study analyzed the potential of eight operational mining sites in Uzbekistan for the installation of photovoltaic (PV) systems: Sarmich, Ingichka, Kuytosh, Yakhton, Chauli, Sherobod, Chorkesar, and Tebinbuloq. A PV system with 1 MW capacity, which required a total of 4926 m2 of project land, was considered. The renewable energy analysis software RETScreen, developed by Natural Resources Canada (NRC), was used to calculate energy production, greenhouse gas reduction, and financial factors of the PV systems in the selected study areas. The iron mine Tebinbuloq in Karakalpak showed the highest potential, with annual electricity production of approximately 1685 MWh, equating to a potential reduction of approximately 930 tons of greenhouse gases. The economic benefit of the PV system in this mine was $2.217 million USD net present value with a project payback period of approximately 13 years. The results of precision checks of satellite- and ground-based solar measurements showed high correlations; hence, satellite-based data can be applied for solar project assessments where solar monitoring meteorological stations are not available.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.247
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 teacher head, not a consensus.

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

Citations20
Published2019
Admission routes1
Has abstractyes

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