Численное моделирование циркуляции атлантических вод в Северном Ледовитом океане
Bibliographic record
Abstract
Warm intermediate waters of the Atlantic origin one of the main features of the Arctic ocean. Two branches of the Atlantic water arrive in the Arctic basin through Norwegian sea. East branch passes through Barents sea where the Atlantic water loses the most part of heat owing to hashing with shelf waters and an intensive exchange through a sea surface. Modified Barents sea water through St. Anna's trench is taken out in the Nansen basin. The second branch of the Atlantic waters -the Spitsbergen current gets into the Arctic basin through the Fram Strait. Mixing up with cold Arctic water, it falls on level of intermediate waters, and follows further along a continental slope in the form of a deep boundary current. This branch of the Atlantic waters is considered as the heat source in the Arctic basin; with its variability connect processes of warming and cooling of waters of Arctic Ocean. The purpose of represented research was reproduction of system of the currents which are responsible for the processes of interaction of water masses of North Atlantic and Arctic oceans on the basis of numerical modeling. We are performing a set of numerical experiments in order to better simulate the circulation of Atlantic Water in the Arctic Ocean by employing a coupled ice-ocean Arctic regional model. The questions, concerning descriptions of advection in ocean models are discussed. Results of use additional parameterizations of subscale processes which have been not resolved in numerical model (among them parametrization of eddy-topographical interaction and isopycnal mixing) are analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".