Bibliographic record
Abstract
The dissertation aims to evaluate how and to what extent e-navigation contributes to reducing accidents for SOLAS ships as well as non-SOLAS ships, hoping that the results are referred to IMO Member States when they are implementing enavigation along with the maritime sectors such as shipping companies, crews on board ships and manufactures developing e-navigation related systems. The study focuses on the potential effects of e-navigation based on tool kits of the IMO e-navigation for SOLAS ships and services of SMART-navigation, which is the Korean approach to implementing the e-navigation concept for both SOLAS ships and non-SOLAS ships. The processes and the methodologies that are used by the IMO to assess the effects of e-navigation are investigated. The vessel accidents for all ships in Korean waters and all Korean-flagged ships worldwide during the 5 years from 2009 to 2013 are analyzed. The formula is proposed to calculate the effects of e-navigation on reducing accidents, which can also be used by other Member States of the IMO when they implement e-navigation in their waters. The direct causes of accidents, which are reducible by the risk control options (RCOs), and the RCOs, which are applicable to non-SOLAS ships, are identified. Additionally, an expert questionnaire survey is carried out with a view to supporting the validity of identifying the RCOs and the direct causes. The results are collated and evaluated for the potential effects of e-navigation on reducing accidents, in relation to type of accidents as well as type of ships, for comparison with the results obtained by the IMO and for reference of other Member States. The concluding chapter examines the results of analysis of e-navigation's tool kits and methodologies to assess their effects on reducing accidents, and discusses the potential rate of accident reduction through e-navigation. A number of recommendations are made concerning the need for further investigation in quantifying the coefficient applied to the proposed formula for evaluating the effects of e-navigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".