Bibliographic record
Abstract
Abstract. Sea ice deformation and dynamics play a significant role in atmosphere-ice-ocean coupling. Deformation patterns in sea ice can be observed at a wide range of spatial scales, though high resolution objective quantification of these features remains difficult. In an effort to better understand local deformation of sea ice, we adapt the Trajectory Stretching Exponents (TSEs), quasi-objective measures of Lagrangian stretching in continuous media, to sea ice buoy data, and develop a temporal analysis of TSE time series. TSEs provide an approximation of Lagrangian coherent structure diagnostics when only sparse trajectory data is available. As TSEs do not require multiple buoys, we find they have an expanded range of use when compared with traditional Eulerian buoy-array deformation metrics, and provide local-stretching information below the length-scales possible when averaging over buoy-arrays. We verify the ability of TSEs to temporally and spatially identify dynamic fracture events for three different sea ice datasets, with buoys identifying fracture domains ranging tens to hundreds of kilometers in diameter.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.008 |
| Insufficient payload (model declined to judge) | 0.247 | 0.174 |
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".