Bibliographic record
Abstract
We define a new classification for Arctic sea ice dynamics based on a spatial and temporal scale:floe, multifloe, aggregate, coherent, sub-basin and seasonal. The classification is supported by remotesensing and in situ observations of ice motions at scales of 1—700 km, as found in the existing scientificliterature. The first significant change in sea ice behavior appears as an “emergent” property of the seaice at the transition from the multifloe scale (2—10 km/< 1 d) to the aggregate scale (10—75 km/1—3 d).This emergent behavior establishes a statistical mechanical length where sea ice can be considered aplastic continuum. A second important, or coherent scale occurs at 75—300 km and 3—7 d, where thespatial/temporal processes of sea ice dynamics best match the scales of the wind forcing, i.e., winds ofthis duration and fetch are necessary to fully load the internal stress field. At scales smaller than thecoherent scale, the spatial dimension is important because the sea ice motions on the coherent scaleprovide non-local forcing to the aggregate scale. At dimensions larger than the coherent scale, includingthe sub-basin and seasonal scales, spatial and temporal averaging occurs, which smooths discontinuities.To understand and model sea ice dynamics at each of these scales requires an understanding of thedetail at the next smallest level. Proper understanding and representation of sea ice dynamics at allscales is critical to devising a sound strategy for data assimilation into 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".