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PERANCANGAN PLTMH UNTUK MENDUKUNG WISATA HIJAU DI BENDUNGAN TAMBLANG, DESA SAWAN, KABUPATEN BULELENG

2023· article· en· W4390864018 on OpenAlexaff
K Fajar Edy Putra Dewantara, I Nyoman Setiawan, I Wayan Sukerayasa

Bibliographic record

VenueJurnal SPEKTRUM · 2023
Typearticle
Languageen
FieldComputer Science
TopicEnvironmental Engineering and Cultural Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsRenewable energyTourismInvestment (military)Power stationBusinessEnvironmental scienceGeographyEnvironmental economicsCivil engineeringWater resource managementEngineeringEconomics

Abstract

fetched live from OpenAlex

The potential of water energy in Bali Province, especially in Tamblang Dam, is very high and can be utilized to provide energy needs in the surrounding area. On the other side, the Buleleng Regency Government took the initiative to develop tourism potential by planning a green tourism-based Tourism Destination Area (DTW), which utilizes renewable energy as a supporting component. One of the renewable energy potentials that can be utilized in Tamblang Dam is Micro Hydro Power Plant (PLTMH), which is designed with a designed flow rate of 2.21 m3/s with a capacity of 1.2 MW. The design of Micro Hydro Power Plant of Tamblang Dam requires an initial investment cost of Rp 2,434,358,091, and based on the results of economic analysis, the design of Micro Hydro Power Plant of Tamblang Dam is classified as feasible for investment, with NPV of Rp 68.6 billion, BCR 40.81, and PP less than 10 years.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0380.006

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 head0.014
GPT teacher head0.210
Teacher spread0.197 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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