Temporal-Spatial Feature of Secular Variation of Global Land Rainfall for June-August during 1948-2001
Bibliographic record
Abstract
The temporal spatial feature of secular variation of global land rainfall fields for June August during 1948-2001 is investigated based on the global land monthly data PREC/L.The result shows that the maximum rainfall for June August happened in the several famous monsoon areas,and their standard deviation is bigger in monsoon areas than the others.The global land rainfall of June August represents a main feature of the negative trend variation,The regions with significant reduction of rainfall include tropical Africa,north of Huaihe River of China,the eastern,central and western Siberian of Russia,North Korea and the South Asian.While,those with increase of precipitation are the northern Canada,Brazil,central Greenland and etc.The trend of coefficients in 12 latitudinal zones is significant with 95% of confidence level except one (65～60°S) with positive trend.The areas with positive trend coefficient are very small.The relation between the trend variation of global land rainfall for June August and ENSO is preliminary discussed.
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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 | 0.000 | 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".