Drought characteristics over China during 1980–2015
Bibliographic record
Abstract
The self‐calibrating Palmer drought severity index (scPDSI) was computed based on the four atmospheric reanalysis data sets combined with observational data over China during 1980–2015. The discrepancies of scPDSI among the four reanalysis data sets show the necessity of integrating multiple data sets. Drought characteristics, such as drought area, severity, duration, and frequency were examined based on multi‐data set mean scPDSI. The results reveal that significant drying trends are found in Qinghai‐Tibet Plateau, southwest, southeast and entire China. Drought area (drought severity) has increased (decreased) by about 1.16% (0.015%) per decade over entire China. Trends in drought duration, temporally averaged severity and frequency also indicate that droughts become more serious in each region during the past 36 years. The identification of drought events in each month by the clustering algorithm shows that droughts over China are more and more frequent. In addition, 65 separate drought events with the duration longer than 3 months were identified under the area threshold of 150,000 km 2 . Through severity‐area‐duration analysis, the 2005–2015 drought is found to be the prominent event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 0.001 |
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 teacher head, 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".