Bayesian Network for Assessing Performance in Complex Navigation - A Conceptual Model
Bibliographic record
Abstract
The maritime industry is considered the backbone of the global economy, enabling efficient transport of goods with a commercial value of about $829 Billion USD through a network of over 90,000 ships, and employing approximately 1.6 million people worldwide. Maritime navigation is a complex and dynamic operational process. On a vessel, demands are put on the human operators (e.g., the captain) with regards to their competence and task execution, as well as the reliability of the technical system. Understanding and assessing human performance is imperative to optimize individual factors for safe and efficient voyages. The human operator and the technical system are interdependent for ensuring this, and sociotechnical characteristics of the system and operations should thus be considered in the assessment. Recent accidents call for a further understanding of this interdependent relationship between socio-related and technical aspects of maritime operations. This paper proposes a conceptual framework using a Bayesian Network and expert elicitation methods to systematically organize maritime navigational demands.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".