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A comparative study on security features of excise stamp of Nepal, Russia, Canada, India, European Union and USA

2022· article· en· W4323915351 on OpenAlexaboutno aff
Gavivi Pathak, Manisha Mann

Bibliographic record

VenueInternational Journal of Medical Toxicology & Legal Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMetallurgy and Cultural Artifacts
Canadian institutionsnot available
Fundersnot available
KeywordsExciseEuropean unionGeographySocioeconomicsPolitical scienceInternational tradeBusinessLawEconomics

Abstract

fetched live from OpenAlex

The following review paper is titled Security features present in excise stamp (tobacco and liquor). The following features are discussed in the paper-Two-toned watermark, Security fibres, Stamp foil, Security thread, Polarised filter, Color shift ink, Serial number, Anti peeling feature, Background image, Anti photocopying feature, Intaglio printing, UV-Visible ink, Quadro-fluorescent ink, Jura ICI feature, IR ink, Micro text, Machine detectable feature, Guilloche pattern, Bi fluorescent ink, Hologram, Hidden text and QR code. Six different countries were undertaken- Nepal, Russia, Canada, India, European Union and the USA, security features present in their excise stamps were put in the form of a rubrics table. Out of 22 security features, it was found that Nepal had the maximum number of security features 14 out of 22 features present, namely- Security fibres, Serial number, Anti peeling feature, Background image, Intaglio printing, UV-Visible ink, IR ink, Micro text, Machine detectable feature, Guilloche pattern, Hidden text, Bi fluorescent ink, Hologram and QR code. European Union followed 13 out of 22 features present, namely, Security Fibres, Stamp foil, Serial number, Background image, UV-Visible ink, Quadro fluorescent ink, Jura ICI feature, IR ink, Micro text, Machine Detectable features, Guilloche pattern, Hologram and QR code. Russia is coming next with 12 out of 22 features, namely- Two toned watermark, Security fibres, Stamp foil, Security thread, Polarised filter, Color shift ink, Serial number, Background image, UV-Visible ink, Micro text, Hologram and QR code. Followed by India with 10 out of 22 features: Stamp foil, Serial number, Anti peeling feature, Background image, intaglio printing, Micro text, Machine detectable feature, Guilloche pattern, Hologram and QR code. Canada is coming next with 8 out of 22 features, namely- Color shift ink, Serial number, Background image, Anti photocopying feature, Intaglio printing, UV-Visible printing, Micro text and Guilloche pattern. Lastly, the USA has 6 out of 22 features: Polarised filter, Color shift ink, Serial number Anti peeling feature, Background image and Micro text.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.364
Teacher spread0.330 · 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 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
Published2022
Admission routes1
Has abstractyes

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