Drought severity and change in Xinjiang, China, over 1961–2013
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monthly climatic data from 53 sites across Xinjiang, China, were used to compare drought severity from the widely accepted Standardized Precipitation Index (SPI) with the recently proposed Standardized Precipitation Evapotranspiration Index (SPEI), as well as trends in the data from 1961 to 2013. Monthly Thornthwaite based (ETo.TW) and Penman-Monteith based reference evapotranspiration (ETo.PM) were computed and subsequently used to estimate SPEITW and SPEIPM, respectively. The indices' sensitivity, spatiotemporal distributions and trends were analyzed. The results showed that the TW equation underestimated ETo, which affected the accuracy of the SPEI estimation. Greater consistency was found between SPI and SPEIPM than between SPI and SPEITW at different timescales. SPI and SPEIPM were sensitive to precipitation, but SPEITW and SPEIPM were insensitive to ETo. The scope of spatial SPEIPM was wider than that of SPI at the same timescale. Obvious differences in SPI, SPEITW and SPEIPM existed between northern and southern Xinjiang. SPEIPM was a better indicator of global warming than SPI. Both SPI and SPEIPM had increasing trends, which contradict previously reported trends in global drought. In conclusion, the decrease in drought severity observed over the last 53 years may indicate some relief in the water utilization crisis in Xinjiang, China.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 it