Evolution of the Arctic Ocean Salinity, 2007–08: Contrast between the Canadian and the Eurasian Basins
Bibliographic record
Abstract
Abstract The authors investigate the variability of salinity in the Arctic Ocean and in the Nordic and Labrador Seas over recent years to see how the freshwater balance in the Arctic and the exchanges with the North Atlantic have been affected by the recent important sea ice melting, especially during the 2007 sea ice extent minimum. The Global Ocean Reanalysis and Simulations (GLORYS1) global ocean reanalysis based on a global coupled ocean–sea ice model with an average of 12-km grid resolution in the Arctic Ocean is used in this regard. Although no sea ice data and no data under sea ice are assimilated, simulation over the 2001–09 period is shown to represent fairly well the 2007 sea ice event and the different components accounting for the ocean and sea ice freshwater budget, compared to available observations. In the reanalysis, the 2007 sea ice minimum is due to an increase of the sea ice export through Fram Strait (25%) and an important sea ice melt in the Arctic (75%). Liquid freshwater is accumulated in the Beaufort gyre after 2002, in agreement with recent observations, and it is shown that this accumulation is due to both the sea ice melt and a spatial redistribution of the freshwater content in the Canadian Basin. In the Eurasian Basin, a very contrasting situation is found with an increase of the salinity. The effect of the sea ice melt is counterbalanced by an increase of the Atlantic inflow and a modification of the circulation north of Fram Strait after 2007. The authors suggest that a strong anomaly of the atmospheric conditions was responsible for this change of the circulation.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".