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Record W4378981386 · doi:10.18280/ijsse.130211

Safety Improvement Model in Small Multipurpose Ports

2023· article· en· W4378981386 on OpenAlexvenueno aff
Santospriadi Santospriadi, Tri Tjahjono, Sunaryo Sunaryo

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer scienceTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Port safety assessment are primarily conducted only on large ports despite the essential roles of small multipurpose ports for archipelagic areas such as east Indonesia.It is important to note that the increase in the activities of the small port is causing an increased risk of accidents.The study aims to assess the risk of accidents at small multipurpose ports.The safety assessment model applies the Formal Safety Assessment (FSA) method of the International Maritime Organization (IMO) and the use of As Low As Reasonably Practicable (ALARP) with adjusting conditions in a small multipurpose port.The expert judgment method supported by study literature, Focus Group Discussion, interviews, observation, and onboard is carried out to obtain data.Validation of risk identification with Eigen index and risk assessment supported by AHP and FTA weighting methods.This study found that incidents of burning ships and human accidents, i.e. people falling off ships or docks, being hit by vehicles, and being hit by mooring line throws, were the highest risks in small multipurpose ports.Cost-benefit analysis using Cost of Averting Fatality (CAF) that several recommendations are obtained to overcome various accidents at small multipurpose ports, which in essence, is accountable and easy-to-implement system work.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.309
Teacher spread0.277 · 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 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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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