Comparative Analysis of Cold Events Over Central and Eastern China Associated with Arctic Warming in Early 2008 and 2016
Bibliographic record
Abstract
This study investigates the possible reasons for the cold events over central and eastern China (CEC) in early 2008 and 2016. The Arctic is dramatically warming, in particular over the Barents–Kara Seas region and notable cold events were observed over CEC although La Niña and El Niño events occurred in early 2008 and 2016, respectively. At the same time, the westerlies decelerated at middle latitudes and the Ural Mountains Blocking High and the Siberian High strengthened. Subsequent analyses indicate that the cold events and the related atmospheric circulation anomalies are closely associated with Arctic warming. Additionally, the cold (warm) phase of the El Niño–Southern Oscillation causes the East Asian winter monsoon (EAWM) to strengthen (weaken) and correspondingly intensify (mitigate) the cold events over CEC. In brief, the Arctic warming is superimposed on La Niña or El Niño events by thermal and dynamic processes, which impact the relevant atmospheric circulation and EAWM and then induce the cold events over CEC in early 2008 and 2016 although their intensity and coverage are also different to some extent.
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 | 0.000 |
| 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".