A systems approach to hazard identification for solar-powered and wave-propelled unmanned surface vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Decarbonization is a trend in the maritime industry and may include the use of alternative energy sources on ships.At the same time, autonomous ships are under development.In the future, the two technologies may be combined.The objective of this study is to identify possible hazards related to the operation of autonomous vessels using green energy sources.An extended and holistic Systems-Theoretic Process Analysis (STPA) based approach is proposed, where both safety and security is considered.Changes in level of autonomy during operation are considered, and an extension of the STPA method is proposed to highlight the interaction between the system and external energy source.A solar-powered and wave-propelled unmanned surface vehicle is analysed.The results show that mission performance may be affected by both safety and security issues, and that considering influences from the environment and the autonomous functionalities of the system together, contributes to identifying hazards.The results are compared to operational experience from multiple field campaigns.The case study focuses on a relatively simple autonomous vehicle, but some functionalities may be shared with Maritime Autonomous Surface Ships (MASS).Hence, implications for utilisation of alternative energy sources on MASS, and effects on risks, are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it