Marginal ice zone dynamics: history, definitions and research perspectives
Bibliographic record
Abstract
Despite enormous scientific and technological progress in numerical weather and climate prediction, sea ice still remains unreliably predicted by models, both in short-term forecasting and climate projection applications. The total ice extent in both hemispheres is tied to the location of the ice edge, and consequently to what happens in the portion of the ice cover immediately adjacent to the open ocean that is called the marginal ice zone (MIZ). There is mounting evidence that processes occurring in the MIZ might play an important role in the polar climate of both hemispheres, yet some key physical processes are still missing in models. As sea ice models developed for climate studies are increasingly used for operational forecasting, the missing physics also impede short-term sea ice prediction skills. This paper is a mini-review that provides a historical perspective on how MIZ research has progressed since the 1970s, with a focus on the fundamental importance of the interactions between sea ice and surface gravity waves on sea ice dynamics. Completeness is not achieved, as the body of literature is huge, scattered and rapidly growing, but the intention is to inform future collaborative research efforts to improve our understanding and predictive capabilities of sea ice dynamics in the MIZ. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".