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Record W4386038827 · doi:10.46799/jst.v3i12.653

Tinjauan Aspek Finansial Penggunaan Mobil Listrik dalam Upaya Mendukung Penurunan Emisi Gas CO2

2022· article· en· W4386038827 on OpenAlexaboutno aff
Zakarya Nugraha, Novri Kusumathalhah

Bibliographic record

VenueJurnal Syntax Transformation · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsElectric carsAutomotive engineeringElectric vehiclePoint (geometry)Oil priceInflation (cosmology)Quarter (Canadian coin)BusinessEnvironmental scienceEnvironmental economicsTransport engineeringEconomicsEngineeringMathematicsMonetary economics

Abstract

fetched live from OpenAlex


 
 
 
 ABSTRACT
 The increase in the price of fuel oil (BBM) in the third quarter of 2022 will have a huge impact on the economy of the community. In addition to inflation, which was also followed by an increase in the price of basic commodities, this also reduced people's mobility in the use of oil-fueled vehicles. The government should use this as a momentum to promote the use of electric vehicles. On the other hand, the price of electric vehicles is still relatively high (expensive) when compared to oil-fueled vehicles. Although from a financial perspective, the use of electric vehicles can be more profitable if it is reviewed within a certain period. This can be shown by the lower cost per kilometer between electric vehicles compared to oil-fueled vehicles. From the point of view of cost comparison analysis over a certain period, the use of electric vehicles is more profitable than oil-fueled vehicles. It is hoped that the tendency of the community can be diverted to using electric vehicles. So that it can indirectly reduce the use of oil-fueled vehicles which is in line with efforts to reduce global warming.
 
 
 

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.011
GPT teacher head0.208
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 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
Published2022
Admission routes1
Has abstractyes

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