Effects of damage on the scaling laws of viscous-plastic sea ice
Bibliographic record
Abstract
Sea ice deformations occur along well-defined lines of deformation called linear kinematic features (LKFs), which exhibit complex laws like spatiotemporal scaling. The complexity of these interactions is undeniable, and a desirable sea ice model should represent LKFs adequately since various processes affecting heat, salt, and moisture exchange between the ocean and the atmosphere occur along these LKFs. This multifractal property of LKFs can be seen in observations and models. However, fine-scale LKFs happen at high resolution (0-2 km), and high-resolution models are costly to run, hence the importance of parametrizing these sub-grid phenomena. Different models are more or less in agreement with the observations, and one model, the Maxwell-Elasto-Brittle model (MEB), claims to reproduce the observed spatiotemporal scaling laws better than the standard viscous-plastic model (VP). One reason could be the presence of an ice damage parametrization in the MEB model that has no equivalent in the VP model. Therefore, we include a suitable damage parametrization with advection in the VP model to disentangle the effect of rheology from the effects of damage on the scaling laws. Results show that the deformation statistics in the VP model are influenced by the inclusion of damage in the model. The inclusion of this damage parametrization gives scaling exponents in agreement with the commonly accepted values computed from the RGPS observations, hinting that damage parametrizations play a crucial role in sea ice models.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".