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Record W2616984329 · doi:10.4224/23000988

Survey of technologies for the airport border of the future

2014· article· en· W2616984329 on OpenAlexafffundvenueabout
Mike Culhane

Bibliographic record

VenueNPARC · 2014
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsNational Research Council CanadaInstitut du Savoir Montfort
FundersHealth CanadaCollege of Engineering, Michigan State UniversityUniversità degli Studi di SalernoTsinghua UniversityHarbin Institute of TechnologyUniversity of TwenteYonsei UniversityUniversität SalzburgChinese Academy of SciencesUniversità degli Studi di MilanoWest Virginia UniversityShandong UniversityThales GroupUniversity of Notre DameMichigan State UniversityCarnegie Mellon UniversityPublic Safety CanadaAccenture
KeywordsAuthentication (law)Emerging technologiesBusinessGeographyEngineeringComputer securityComputer science

Abstract

fetched live from OpenAlex

Defence Research and Development Canada's Centre for Security Science (DRDC-CSS) commissioned a literature and patents review by the National Research Council’s Knowledge Management section (NRC- KM) to investigate emerging technologies to support the airport border of the future. The focus of the review is on technologies that enhance and improve the traveller authentication process as well as the screening of passengers and their luggage at the point of entry. The project was divided into three phases according to the three key questions (see Table 1). Phases I and II consisted of a review and summary of the available literature. Phase III involved a survey of technologies currently deployed in airports and those that may emerge in the next 5-10 years, and was divided into two main areas: identity verification (i.e. biometrics) and baggage/passenger screening. Literature and patent databases were searched for each question, and bibliographic records imported to text mining software for analysis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.086

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.233
Teacher spread0.224 · 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 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

Citations3
Published2014
Admission routes4
Has abstractyes

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