БАЛАНС ЛЬДА В СЕВЕРНОМ ЛЕДОВИТОМ ОКЕАНЕ В 1979-2019 ГГ. (ПО ДАННЫМ МОДЕЛИРОВАНИЯ)
Bibliographic record
Abstract
The results of numerical experiments on the model of joint water and ice circulation for the period from September 1979 to December 2019, aimed at studying the interannual variability of the ice balance in the Arctic Ocean, are considered. The results obtained made it possible to analyze the geographical features of the processes of ice formation and melting in the Arctic Ocean and to identify key areas that determine the volume of ice in the ocean. It is established that the main quantity of ice is formed in waters of the Siberian seas, and the most intense melting occurs in the North European Basin, where the ice transported by the Transpolar Current through the Fram Strait enters the relatively warm water of the Greenland Sea, heated by the North Atlantic Current. The formation of the absolute minimum of ice coverage in 2012 was promoted by the anomalous position of the anticyclonic hydrological cycle – much closer to the Canadian coast. This resulted in the fact that only a small part of the ice formed in the Siberian seas was involved into a weakened circulation, while most of the ice in the stream of the Transpolar Current was transported through the Fram Strait to the Greenland Sea. Statistical analysis of the results of numerical experiments demonstrated that the trend towards a decrease in the volume of ice in the Arctic Ocean is primarily determined by the global warming, and dynamic forcing exerts significant effect on local extremes.
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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 | 0.001 |
| 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) | 0.005 | 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".