Bibliographic record
Abstract
Abstract. In Arctic sea ice, the intersection angles between Linear Kinematic Features (LKFs) are linked to the internal mechanical properties. Sea ice rheological models struggle to reproduce the intersection angles between LKFs in Arctic sea ice. We aim to obtain an intersection angle distribution (IAD) from observational data to serve as a reference for high-resolution sea ice models and to infer the mechanical properties of the sea ice cover. We use the sea ice vorticity to discriminate between acute and obtuse LKFs intersection angles within two sea ice deformation datasets: the RGPS and a new dataset from the MOSAiC drift experiment. Acute angles dominate the IAD, with single peaks at 48º ± 2 and 45º ± 7. The IAD agrees well between both datasets, despite the difference in scale, time periods, and geographical location. The divergence and shear rates of the LKFs also have the same distribution. The dilatancy angle (the ratio of shear and divergence) is not correlated with the intersection angle. Using the IAD, we infer an internal angle of friction in sea ice of µI= 0.66 ± 0.02 and µI= 0.75 ± 0.05. The shape of the yield curve or the plastic potential derived from the observed IAD resembles the teardrop or a Mohr–Coulomb shape. With those new insights, sea ice rheologies used in models can be adapted or re-designed to improve the representation of sea-ice dynamics.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.609 | 0.449 |
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".