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Record W3048143156 · doi:10.1016/j.xinn.2020.100039

Accelerate the Promotion of Mobile Payments during the COVID-19 Epidemic

2020· article· en· W3048143156 on OpenAlexaboutno aff
Tuanhui Ren, Yu Tang

Bibliographic record

VenueThe Innovation · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Coronavirus disease 2019 (COVID-19)Circulation (fluid dynamics)PaymentCurrencyCoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BusinessCashVirus2019-20 coronavirus outbreakVirologySevere acute respiratory syndrome coronavirusEconomicsMedicineMonetary economicsFinancePolitical scienceEngineeringInternal medicine

Abstract

fetched live from OpenAlex

COVID-19 is caused by a novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). During the COVID-19 epidemic, when people are infected with the virus, they can transmit the virus onto paper or coin money through touch and droplets, potentially making any physical currency a carrier of the virus. Although there is no report confirming that people can become infected with viruses by cash circulation, relevant research on the survival of viruses on solid surfaces supports this hypothesis. Mobile payments can help individuals avoid coming in direct contact with any paper or coin money. Therefore, we strongly recommend the promotion of mobile payments during the COVID-19 epidemic.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0330.010

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 head0.155
GPT teacher head0.314
Teacher spread0.159 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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