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Record W4381431451 · doi:10.32528/elkom.v5i1.8281

Studi Pengaruh Covid-19 Pada Berbagai Sektor Pengguna Tenaga Listrik di Bangka Belitung

2023· article· en· W4381431451 on OpenAlexaboutno aff
Fajar Ramadani, Asmar Asmar, Rika Favoria Gusa, Wahri Sunanda

Bibliographic record

VenueJurnal Teknik Elektro dan Komputasi (ELKOM) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCOVID-19 Prevention and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFellQuarter (Canadian coin)Secondary sector of the economyConsumption (sociology)Government (linguistics)BusinessPrivate sectorElectricityPublic sectorTest (biology)Agricultural economicsEconomicsEngineeringEconomyEconomic growthGeographyCartography

Abstract

fetched live from OpenAlex

Electricity in Indonesia, especially in the Bangka Belitung Islands, has been affected by the COVID-19 pandemic. The effect occurred at the beginning of the pandemic in the second quarter of 2020, namely the household sector rose to 3.3%, the social sector fell -3.1%, the industrial sector rose to 15%, business fell -9.8%, the office sector government buildings rose 3%, and the public/other street lighting sector fell -47%. By using statistical tests on the classical assumption test all sectors are normally distributed. In the partial test using multiple linear regression test for the household sector, industry, and government office buildings, only connected power affects energy consumption, namely 70.5%, 97.7%, and 37.4%. In the social and business sectors, there is an effect of connected power and tariff prices on energy consumption, namely 92% and -13%, while business is 42% and 10%, respectively. By using the simultaneous test, the household sector has an effect of 68.2%, the social sector is 75.5%, the industrial sector is 97.5%, the business sector is 44.2% and government buildings are 32.5%.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.070
GPT teacher head0.395
Teacher spread0.325 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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