Bibliographic record
Abstract
Abstract In viscous‐plastic (VP) sea‐ice models, small deformations are approximated by irreversible viscous deformations, introducing a nonphysical energy sink. As the spatial resolution and the degree of numerical convergence of the models increase, linear kinematic features (LKFs) are better resolved and more states of stress lie in the viscous regime. Energy dissipation in this nonphysical viscous regime therefore increases. We derive a complete kinetic energy (KE) balance for sea ice, including plastic and viscous energy sinks to study energy dissipation. The main KE balance is between the energy input by the wind and the dissipation by the water drag and the internal stresses (dissipating 87% and 13% of the energy input on an annual average). The internal stress term is mostly important in winter when ice‐ice interactions are dominant. The energy input that is not dissipated locally is redistributed laterally by the internal stresses into regions of dissipation by small‐scale deformations (LKFs). Of the 13% dissipated annually by the internal stress term, 93% is dissipated in plastic friction along LKFs (14% in ridging, 79% in shearing) and 7% is stored as potential energy in ridges. For all time and spatial scales tested, the frictional viscous dissipation is negligible in the KE balance. This conclusion remains valid regardless of the degree of numerical convergence of the simulations. Overall, the results confirm the applicability, from an energetical point of view, of the VP approximation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".