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Record W2988252916 · doi:10.33889/ijmems.2020.5.1.012

A Glance at Transit System Safety

2019· article· en· W2988252916 on OpenAlexaff
James Li

Bibliographic record

VenueInternational Journal of Mathematical Engineering and Management Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsTransport Canada
Fundersnot available
KeywordsRisk analysis (engineering)System safetySafety assuranceFunctional safetyEngineeringManagement systemReliability engineeringSafety engineeringTransport engineeringComputer scienceOperations managementBusiness

Abstract

fetched live from OpenAlex

System safety is a discipline of applying engineering and management principles, criteria, and techniques to achieve acceptable or tolerable risk within the constraints of operational effectiveness, suitability, time, and cost throughout all phases of the system life. System safety engineering is the program to identify hazards, and to eliminate hazards or reduce the associated risks when the hazards cannot be eliminated. System safety management involves plans and activities taken to identify hazards; assess and mitigate associated risks; track, control, close, and document risks encountered in the design, development, test, manufacturing, installation, operation and maintenance, and the disposal of systems, subsystems, and equipment. In this paper, the concept and principle of system safety in the transit system is discussed. The paper also introduces the safety standards, safety life-cycle, Safety Integrity Levels (SILs), safety analysis techniques and safety cases etc.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0040.012
Insufficient payload (model declined to judge)0.0220.008

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueInternational Journal of Mathematical Engineering and Management SciencesSame topicSafety Systems Engineering in AutonomyFrench-language works237,207