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Record W4312142086 · doi:10.54097/hbem.v4i.3358

Analysis of Current Situation, Problems and Potential Solutions of Electric Vehicle Industry

2022· article· en· W4312142086 on OpenAlexaff
Yuan Lü, Yong Ge, Yuying Chai

Bibliographic record

VenueHighlights in Business Economics and Management · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsWestern University
Fundersnot available
KeywordsBattery (electricity)Government (linguistics)Quality (philosophy)Electric vehicleProcess (computing)New energyProduction (economics)Driving rangeRange (aeronautics)BusinessEngineeringRisk analysis (engineering)Manufacturing engineeringTransport engineeringComputer scienceEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

The onset of the 21st century has seen remarkable developments in almost every industry in our modern-day world. Technology develops the quality of life and grows at an unprecedented rate. During the long run, newly type of vehicles is innovated, and new energy vehicles are an important development direction. Electronic vehicle (EV) not only is a production of the new era, but also a process of the evolution. The first EV appeared in late of 1830s which also be the prototype of the EV of nowadays. Currently, EVs are commonly used by individuals, the reason that EVs are more environment friendly, with the fuel less costly, and get government’ beneficially. However, EVs still have issues in nowadays, i.e., installation, short range, and battery handling. To solve these issues, the public could offer more installation charging piles, and EV companies could improve the battery capacity and maintenance. Overall, the reports will illustrate the current status and current problems that EVs faced, also showing the solutions and the recommendations to help the EV industry to develop.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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