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Record W4406015818 · doi:10.18280/ijsse.140602

A Novel Method to Improve the Defense Security of Railway Transportation

2024· article· en· W4406015818 on OpenAlexvenueno aff
Zsolt Lévai

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2024
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsComputer securityTransport engineeringPoison controlComputer scienceEngineeringForensic engineeringMedical emergencyMedicine

Abstract

fetched live from OpenAlex

Rail transport is used not only by the civil sector but also by military transportation.This duality makes it crucial to maintain the functionality and security of the system.This research aims to determine the combined impact on maintaining the sector's functionality by analyzing the elements of civil transport safety and military defense security.The result of the research is the definition of a new evaluation index (RDSI) as a synthesis of the security and security factors, which can consider transport safety and defense security requirements.The calculated value of the index provides an idea of the civil safety and military security improvements needed on a railway line, thus increasing the level of protection and resilience of a country.Further use of the index can help to identify weak points of the rail transport system, give an idea of the protective utility of a railway line, find bypasses, and improve the impact of climate change on rail transport.The effectiveness of the index is demonstrated through a case 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 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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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