Model Ice: A Review of its Capacity and Identification of Knowledge Gaps
Bibliographic record
Abstract
This paper presents a review of the state of the art of model-scale ice with a focus on mechanical behavior, mechanical testing and scaling. The goal of the model-scale ice production is the generation of a material that can represent sea ice in as many aspects as possible. The question therefore is, to what extent model-scale ice complies with this high level requirement and what possible limitations of model-scale ice are encountered. A part of the answer lies in the history of model-scale testing in ice as model ice was originally designed to test ships in ice. The interaction of ships with ice, when breaking level ice, differs from other interaction scenarios in terms of triggered failure processes and consequently in the relevant mechanical properties of the ice. The significance of the forces in particular interaction scenarios is reflected in the applied scaling laws. The standard scaling laws are presented together with published alternatives and their limitation and practicality is evaluated. The relevant interaction forces and ice properties are compiled for level ice breaking, ships in brash ice and offshore structures in slow ice drift. In the production of model-scale ice, the mechanical properties and their measurements play a significant role, as they determine how well the full-scale scenario is scaled. However, latest research in context with earlier published findings indicates that state of the art measurement procedures may not be able to capture the actual ice properties, as the mechanical behavior of model-scale ice might be different than generally presumed. Consequently, this paper presents alternative measurement procedures and highlights existing knowledge gaps which are worthwhile to be addressed in future.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".