Considerable Arctic Sea ice loss as a factor of cold weather and heavy snowfalls in Eurasia
Bibliographic record
Abstract
Abstract There goes climate warming on the Earth. An especially tremendous warming goes in the Arctic. This causes shrinking of the sea ice extent in the Arctic. Considerable Arctic Sea ice loss can lead to some extra evaporation of water vapor from the sea surface and saturation of the atmosphere with water vapor. Due to atmospheric circulation, extreme temperature anomalies and heavy snowfalls can appear in the following winter season in the lower latitudes, according to a number of studies. Some warm winters in Eurasia and America are associated with a situation in the Arctic where an extremely stable area of low pressure in the vicinity of the North Pole was present during a long time period and did not let cold air masses to move away from its borders. As a consequence, in most of the territory of Russia, USA, Northern Europe, and Eastern Canada the temperature of the winter months was a few degrees more than the usual one, for example in 2019/2020 winter months. The resulting destruction of the North Pole vortex and coming of cold air masses to Eurasia and America lead to a cold snowy winter.
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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) | 0.002 | 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".