Challenges of the Digital Transformation for Shipping: Human-Centered Design for Marine Navigation Systems
Bibliographic record
Abstract
Abstract The digital transformation of the marine industry presents opportunities and challenges for engineers, designers, and seafarers alike. These challenges are specifically acute in the context of the Arctic, where the presence of sea ice, severe metocean conditions, and limitations to modern charting and navigational aids present increased navigational challenges. Integration and further development of digital bridge tools can improve safety, but must take into account human factors concerns by drawing from literature and case studies where the human-machine interaction has broken down, leading to incidents. In this paper, the challenges of using digital bridge tools to support safe Arctic navigation are discussed as they relate to safe human-machine interaction. A case study of a passenger ship grounding in the Canadian Arctic is presented to demonstrate how failures in bridge equipment design and operation can contribute to accidents. This case study is discussed in relation to literature on human factors engineering and compared to other incidents where human factors was listed as a potential cause. These findings can be used to inform designers of marine navigation systems of the best practices to be aware of when implementing new technologies on the bridge of ships. Additionally, the implications of these findings on autonomous ship development and operation are discussed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".