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Record W3109898805 · doi:10.12972/ksmer.2014.51.4.525

A Comparison of Wind Power and Photovoltaic Potentials at Yeongok, Mulno and Booyoung Abandoned Mines in Kangwon Province, Korea

2014· article· en· W3109898805 on OpenAlexaboutno aff
Jinyoung Song, Yosoon Choi, Mihyang Jang, Suk-Ho Yoon

Bibliographic record

VenueJournal of the Korean Society of Mineral and Energy Resources Engineers · 2014
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemEnvironmental scienceMining engineeringGeologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This study assessed and compared wind power and photovoltaic potentials at the Yeongok, Mulno and Booyoung abandoned mines in the Kangwon province, Korea. Resources data of wind and solar for each abandoned mine were collected from the renewable energy data center in Korea Institute of Energy Research(KIER) and Korea Meteorological Administration(KMA). RETScreen software developed by Natural Resources Canada(NRC) was utilized for analyzing the electricity productions, reductions of greenhouse gas emission and net present values of the 600 kW wind power and photovoltaic systems at the three abandoned mines. As a result, we could know that the wind power potentials are higher than the photovoltaic potentials at the Yeongok and Mulno abandoned mines. However, the photovoltaic potential is higher than the wind power potential at the Booyoung abandoned mine when considers the net present value.

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)0.000
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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