The melt‐freeze cycle of the Arctic Ocean ice cover and its dependence on ocean stratification
Bibliographic record
Abstract
[1] A time-dependent, 1-D coupled ice-ocean model is used to quantify the impact of ocean stratification on the Arctic ice cover. The model results show that the ice growth during winter equals the ice melt in summer for areas with a well-developed cold halocline layer (CHL), provided that the initial ice thickness is around 3 m, while thinner initial ice thickness results in net growth. Areas with weak salt stratification can have a negative annual thickness change irrespective of the initial ice thickness and are thus dependent on ice import in order to remain ice covered. The model results also show that ocean stratification is mostly important for ice-thickness development during the growing season. Areas with weak stratification have an ocean heat flux up to 8 W m−2 reaching the ice during the growing season, while areas with a CHL have an average of about 0.7 W m−2. In the extreme area, north of Svalbard, the ocean heat fluxes are typically around 25 W m−2 but can be up to 400 W m−2 during the initial adjustment, when the warm Atlantic water has direct contact with the ice. A general outcome of the study is that, depending on ocean stratification, the ice cover of Arctic Ocean can be divided into one part with net ice growth (the major part) and another part with net ice melt (mainly in the Nansen Basin).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".