Climatic variation characteristics in cold winter over Northeast China,1961—2010
Bibliographic record
Abstract
According to the China national standard of warm winter grade,define the grade of single station and regional cold winter(CW)and propose the CW intensity index(CWII)and the CW severity index(CWSI).Based on the recorded data of 90 meteorological stations over Northeast China during 1961—2010,analyze the spatio-temporal variations of CW.The results show that the mean temperature of each station in winter shows a clear increasing trend with the value of 0.02℃~0.94℃ per ten years over Northeast China in the last 50 years.The positive amplitude gradually increases from west to east.The mean temperature of the entire region in winter also shows an obvious increasing trend with the value of 0.55℃ per ten years.The CW frequency for single station is 30%~48% with a decreasing trend on the southeast-northwest-northeast direction.CWII of each station is within the range of 1.18~2.20 over Northeast China in the last 50years.However,the distribution of CWII is complex and shows a non-obvious spatial pattern.CWSI of each station is in the range of 0.22~0.68 with a weakened trend on the southeast-northwest-northeast direction.Regional cold winter index has an obvious decreasing trend with the value of-10.47% per ten years.The change trend is significant at the 0.05 confidence level.La Nina phenomenon may not have obvious effect on the occurrence of CW in Northeast China.The Arctic Oscillation negative anomalies may be the main reason for the cold winter events over Northeast China.
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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 | 0.000 |
| 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".