MétaCan
Menu
Back to cohort

VPass: An Open-Source COVID-19 Vaccine Passport, and Vaccine Hesitancy

2022· article· en· W4352981167 on OpenAlexaff
Ravi Kansagara, ANK Zaman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsConestoga College
Fundersnot available
KeywordsComputer scienceCertificateEncryptionCoronavirus disease 2019 (COVID-19)The InternetWorld Wide WebComputer securityInternet privacyMedicine

Abstract

fetched live from OpenAlex

The use of technology is one of the keys to combating the covid-19 pandemic. This paper proposes and demonstrates an implementation of a digital vaccine passport /certificate is, called VPass, for taking non-essential services. This passport will represent someone's vaccination status while preserving all personal data safe. The developed application is platform-independent and accessible using any device connected to the internet. This application also keeps an offline copy in a device or a printed copy of a vaccine passport for convenience. A quick response (QR) code will show the COVID-19 vaccination status. All data stored and transmitted between the front (to the end user) and backend (to and from the server) are fully encrypted. This paper presents the technical detail of implementing a digital vaccine passport for COVID-19. This application could also be used for keeping other vaccination records/certificates.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
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 communication0.0010.003
Open science0.0030.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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

Explore more

Same topicCOVID-19 Digital Contact TracingFrench-language works237,207