A state-of-the-art review on ice modeling methodologies employed in refrigerated ice tanks
Bibliographic record
Abstract
Recent exploration of natural resources and their transportation in the arctic or sub-arctic regions and the increase of traffic through the Northern Sea Route (NSR) have stimulated new structural concepts and ship designs. To evaluate the design and performance of these structures and ships, model tests in an ice tank are the preferred evaluation tool. The Maritime Ocean Engineering Research Institute (MOERI) is building the first ice tank in Korea to meet the increasing research and development challenges and opportunities arisen from the recent demands. This study mainly reviews the state-of-the-art ice modeling techniques and methodologies used in existing refrigerated model basins to assist their adaptation to the new ice tank. The physical and mechanical properties of different types of model ice and their scalability are critically assessed. Comparisons of mechanical properties from sea ice and model ice are presented and discussed. This report also briefly discusses scaling issues for ice model tests, test methodologies for ship performances in ice, and the ice testing facilities at IOT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".