Safety Improvement Model in Small Multipurpose Ports
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".