Quality of Navigational Safety in the Inland Waterway Transport System of the Musi River: Seafarer’s Perceptions
Bibliographic record
Abstract
The Musi River in South Sumatra is a significant inland waterway for coal and other waterborne transportation. The river has seen a rise in maritime accidents, especially in recent years. Human error, inadequate communication, a lack of navigational aids, and challenging hydrographic conditions are commonly blamed for these incidents. Current research and data regarding the condition of navigational systems, especially from the perspective of seafarers operating on the Musi River, are limited. This study aims to analyze the quality of navigational safety in the inland waterway transport system of the Musi River, one of the inland waterways. This study focuses on seafarers' perceptions of navigational infrastructure and communication quality related to their safety perceptions along the Musi River. The study involved 53 seafarers who provided their perceptions of the quality of navigation equipment and communication along the Musi River fairway. Responses were collected through a questionnaire using purposive sampling. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed for data analysis, including inner and outer model analyses and significance testing via bootstrapping. The results showed that navigational infrastructure and communication quality positively influenced seafarers’ safety perceptions. They also show that seafarers feel a certain degree of safety when crossing the Musi River, which is commonly in “good” condition. This study is a preliminary step to gathering additional data on navigational conditions in other areas. Further research could explore the implications of various variables, such as human and natural factors, technology, and seasonal weather patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".