Lifecycle And Virtual Prototyping Requirements For Ship Repair Projects
Bibliographic record
Abstract
Repair yards need new tools and paradigms to integrate the different design stages through more standardized information and to introduce a reliable modeling method for lifecycle analyses for retrofitting designs, supporting bid decisions and later lifecycle ship stages. It is crucial that the design tools and paradigms are improved to allow new designs and processes to minimize the total costs of refits. This paper focuses on specifying requirements for integration of rapid virtual prototyping and life cycle tools in the retrofitting design stage of a ship in order to provide insights into developing the optimal data structuring and user interfaces. With the target to support both ways it´s necessary : first the development of new design models which help the shipyard to assess the cost and time modeling for the retrofitting; and second the performance of LCCA, LCA and Risk analysis. The specified requirements will be outputs of the SHIPLYS project and will be used within SHIPLYS project to develop and integrate tools that allow increasing the efficiency, speed and reliability of retrofitting design processes.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".