Impact of Climate Change and Human Activities on Vegetation Coverage in the Mongolian Plateau
Bibliographic record
Abstract
Global climate change and Global Change and Terrestrial Ecosystems( GCTE) are currently the hot and difficult topics in researching global change. Climate change impacts significantly the global ecosystems,and it has become as an important issue to ascertain the interaction between climate change and ecosystems at different scales and reveal the responses of ecosystems to climate change. Based on GIMMS Normalized Difference Vegetation Index( NDVI) data in the Mongolian Plateau during the period from 1981 to 2006,the spatial and temporal variations of vegetation coverage were studied,and the factors affecting the change of vegetation coverage were analyzed from the perspectives of climate change and human activities. Vegetation coverage in the Mongolian Plateau was generally improved in a fluctuation way during the period of 1981- 2006,and the improvement included the following phases: ① Relatively stable phase during the period of 1981- 1988; ② Persistently increasing phase during the period of 1989- 1993; ③ Relatively low phase during the period of 1994- 1998; and ④ Persistently increasing phase during the period of 1999- 2006. There was an obvious zonal spatial and temporal distribution of annual maximum NDVI in the Mongolian Plateau during the period of 1981- 2006,and the NDVI change trend was different from different land use types: The vegetation in forest area with high NDVI value was in degeneration,there was a significant negative correlation between temperature and precipitation,and the area with vegetation regeneration was much smaller than that with vegetation degeneration. The vegetation coverage in farmland with relatively low NDVI value was in a significant increase trend,there was a significant increase trend of both temperature and precipitation,the NDVI value was obviously increased under the dramatic effect of human activities,and the area with vegetation regeneration in farmland and grasslands were much larger than that with vegetation degeneration.
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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 | 0.001 | 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".