Research into the Development of HLA-based Ship Safety Assessment Simulation with Systems Engineering
Bibliographic record
Abstract
Many accidents occur on the oceans. Accidents involving ships can cause enormous damage. When an accident occurs, the cost of environmental restoration can bankrupt even a top-ranked international company. The potential damages resulting from ship accidents have resulted in many international agreements such as MARPOL, SOLAS, ISM Code, etc. The viability of damaged ships has become more and more important. A damaged ship's viability and cargo safety can be improved by a safety assessment design procedure. We consider th￡ shipbuilding industry's distributed characteristics to define a safety assessment system with software engineering-based requirements analysis. We used a distributed network-based simulation method (HLA - High level architecture), because of ease of reuse and expansion of existing components to other situations, and because it is a military standard. HLA is the standard for distributed network-based simulation in many countries including the United States, Canada, Israel, and Korea. The paper describes research to develop a prototype of a network-based safety assessment simulation system by software engineering based analysis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".