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Record W4200213313 · doi:10.22214/ijraset.2021.39302

Effect of Increase in the Price of Petroleum and Increase in the Sales of Electric Scooter

2021· article· en· W4200213313 on OpenAlexaboutno aff
Abhishek Venkateshwar

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingGasolineElectricityQuarter (Canadian coin)BusinessBattery (electricity)JumpPetroleumAgricultural economicsWaste managementEconomicsEngineeringMarketingPower (physics)ChemistryElectrical engineering

Abstract

fetched live from OpenAlex

Abstract: Our paper is among the first to measure the potential effect of increase in the price of petroleum vs increase in the sales of electric scooter. With the prices of petrol inching closer to Rs 100 per liter, the demand for battery operated scooters is on the rise as people want to save on fuel cost. As a result, the sale of e-scooters has increased considerably in the city. buyers of two-wheelers are also evincing interest in purchasing e-scooters and making enquiries about vehicles in the local showrooms Okinawa, a large scooter manufacturer in India, recently reported a 30% sales jump in the last quarter alone. The company attributes this largely to the rise in gasoline prices. Without being tied to a pump, electric motorcycle riders are insulated from fluctuating gasoline prices, and can instead “fill up” on much less expensive electricity from any wall outlet.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.001

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 head0.020
GPT teacher head0.343
Teacher spread0.323 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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