Bibliographic record
Abstract
Ship designers, builders, owners, insurers and class societies are becoming ever more aware of the complex interactions of the various systems found on all types of marine vessels. Therefore a design process that acknowledges these demands and assesses the risks posed, and manages them becomes ever more important. This paper seeks to explore some of the, sometimes apparently, conflicting requirements that are placed on designs of new marine platforms and looks at methods that enable these elements to be expressed, understood and managed in the context of an integrated ship design. The demands placed on new vessels include a range of requirements that move away from being solely based around the traditional functional requirements; including the ideas of designing for ease of shipbuilders, operators and maintainers; and now acknowledging the need of a through life safety case, cyber security case, and full obsolescence planning. This becomes ever more complex when consideration is given to how these through life elements are practically managed, with a range of methods, none of which are without their own challenges. It is important to note as these demands are discussed that often a ‘solution’ in the truest sense does not exist and the management of risk becomes a balance between the expected risk, the practicable solution, along with the potential compromises to both programmes and cost. While these demands place huge constraints and drive complexity into design processes, the issues can, and regularly have, been further exacerbated when some of these, or other requirements, are introduced into the design or build phases of projects. Introduction of design drivers should not be undertaken lightly or without expected, and accepted, increases in required resources, both financial and calendrical.
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.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".