Ridged sea ice characteristics in the Arctic from a coupled multicategory sea ice model
Bibliographic record
Abstract
In this study, a multicategory sea ice model with explicit ice classes for ridged and rafted ice was used to examine the evolution of deformed ice during the period 1980–2002. The results show that (1) ridged ice comprises roughly 45–60% of Arctic sea ice volume and 25–45% of the sea ice area, (2) most of the perennial ice consists of ridged ice, and (3) ridged ice exhibits a small seasonal variability. Our results also show an increase in mean ridged ice thickness of 4–6 cm yr−1 during the summer in an area north of the Canadian Archipelago and a corresponding decrease in the East Siberian Sea and Nansen Basin. At the same time, Arctic sea ice age has been observed to decline and ice drift speed to increase during the simulation period. We connect these findings with a modeled regional increase in the production rate of ridged ice. Comparison of the multicategory model and a two category reference model shows a substantially increased ice production rate due to a more frequent occurrence of leads, resulting in an ice thickness increase of up to 0.8 m. Differences in ice physics between the multicategory and reference models also affect the freshwater content. The sum of liquid and solid freshwater content in the entire Arctic Ocean is about 10% lower and net precipitation (P‐E) is about 7% lower as compared to the reference model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".