A Simple Study on Relation of Winter-Spring Greenland Sea Ice Chang with Air Temperature/Precipitation in Early Summer of China
Bibliographic record
Abstract
The winter-spring mean sea ice extent oscillations nearby Greenland island and their relationships to surface air temperature and precipitation in early summer of China have been examined in the present paper using the GISST sea ice extent dataset released by Hadley center, UK, and NCEP/NCAR 40-year reanalysis as well as the recorded surface air temperature and rainfall in China, empirical orthogonal function (EOF) analysis and wavelet analysis, etc. The results point out that the sea ice extent oscillations on the west and east sides of Greenland island show inverse variation with distinct interannual and interdecadal circles. The winter-spring sea ice extent oscillation over Greenland-Norwegian Sea is positively correlated to June surface air temperature to the north and rainfall to the south along Yangtze River in China, whereas is negatively correlated to the reverse and vice versa to sea ice extent over Davis Strait-Labrador sea. The composed analyses based on the NH 500 hPa general circulation show that the winter-spring sea ice extent oscillations nearby Greenland island are in contact with the persistent Arctic polar vortex and northern hemisphere blocking high anomalies. The sea ice extent oscillations are one of factors to affect precipitation and surface air temperature in early summer of China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".