Validation of Pack Ice Resistance in Oblique Condition by the Comparison With Ice Model Test Results
Bibliographic record
Abstract
Abstract Countries around the world are increasingly interested in resource development in the Arctic due to global warming. Recently, Arctic coastal states (Russia, USA, Canada, etc.) are pursuing infrastructure construction projects for resource development in the Arctic region. Because the offshore structures in the Arctic are exposed to the sea ice, in order to ensure the safety of the structures, the calculation of the ice resistance is of paramount importance for offshore structures. In general, studies on the ice resistance have been carried out for the breaking and clearing performance of icebreakers. However, in the case of fpu (floating production unit) for resource development in the Arctic region, it is necessary to estimate the ice resistance in the oblique condition to ensure safety. Thus, despite estimation of the ice resistance in the oblique condition is significant, there has not been enough research until recently. In this paper, we suggest algorithms for estimating the ice resistance in the oblique condition. For the estimation process, an in-house code software program is used and an ice resistance estimation module is implemented for the oblique condition using empirical formula. This paper shows results of the ice resistance which was calculated in the oblique condition, and the change of the ice resistance is shown according to various oblique angles in pack ice. In addition, the results are compared to the model test result of a fpu in pack ice of 80% concentration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".