Relationship between NDVI and Precipitation and Temperature in Middle Asia during 1982-2002
Why this work is in the frame
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Bibliographic record
Abstract
The five countries in Middle Asia lie in the center of Eurasia. Most part of this region is arid and semi-arid zone with sparse vegetation cover. The study of the vegetation dynamics and environmental change in this region is important to the research of environment and climate in China. This paper explored the vegetation dynamics and its relationship with major climatic factors in middle Asia by using AVHRR-NDVI dataset at 8km spatial resolution and CRU climate data set at 0.5° spatial resolution between 1982 and 2002. These two datasets were unified to the same spatial resolution of 8km and Alberta geographic projection. The trend analysis showed that 53 percent of the land cover was relatively stable, with a very small NDVI change of ±0.005 NDVI per year. These regions, especially the two large deserts, were mainly in the center of Middle Asia. Forty percent of the land had a NDVI up-trend of more than 0.0005 NDVI per year, which was mainly in the north and south of Middle Asia, while only 6 percent of the land had a NDVI down-trend of less than 0.0005 NDVI per year. The analysis on land cover types indicated that evergreen forest and alpine grass (steppe) were among the best up-trend group with NDVI gains more than 0.0014 and 0.0009 per year, while the p values are 0.001 and 0.001 respectively. There were no obvious changes in deciduous forest, grass, crop and steppified desert. To investigate the possible driving forces, correlation analysis was conducted between AVHRR-NDVI and major climatic factors, which are precipitation and temperature. In 49 percent of the area, especially in the forest steppe in north Middle Asia, annual average AVHRR-NDVI was closely related to the annual precipitation, especially that in spring and summer. Only 17.78 percent of the area is related to the annual average temperature with a validation coefficient of more than 0.05. Annually speaking, the positive correlation coefficient of evergreen forest, alpine grass with the annual average temperature is relatively low, with the correlation coefficients of 0.432 and 0.557 as well as p value of 0.052 and 0.009 respectively. The positive correlation coefficient of crop and grass with annual precipitation are comparatively low with R values of 0.511and 0.476 as well as p values of 0.018 and 0.029 respectively. The R value between NDVI and precipitation for deciduous forest was 0.415 in summer and 0.461 in winter, while the p value was 0.01 in summer and 0.461 in winter. The positive correlation coefficient of re-vegetated desert cover with precipitation in spring is relatively lower with the R value of 0.415 and the p value of 0.0061.
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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 | 0.000 | 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) | 0.000 | 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 it