Risk-based Winterization on a North Atlantic-based Ferry Design
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
The Arctic is a recent focal point of the marine and offshore industries. Winterization is required for safe and efficient operations in these harsh cold environments. A riskbased approach to winterization was recently proposed to provide a quantitative way of determining the need for winterization and its appropriate level. To further validate and enhance this approach, it has been applied to a new ice-class passenger ferry design, which will operate in a particular area of the North Atlantic. This location is ideal for the application with low temperatures, strong wind, and high waves. To facilitate this application and eliminate some limitations of the proposed approach, this article proposes a generic framework of risk-based winterization. Results from this article validated the effectiveness and feasibility of using risk-based winterization on vessel designs.
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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 | 0.000 | 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) | 0.001 | 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