Bibliographic record
Abstract
As the best instruction factor of vegetation coverage,NDVI is regarded as the most effective indicator to monitor regional or global change of vegetation and ecological environment. Based on MODIS NDVI data with spatial resolution of 250 m and climate data during the period from 2000 to 2011,in this paper the spatiotemporal change of vegetation coverage and its correlations with climatic factors in the Qilian Mountains were researched using the methods of maximum value composite,trend line analysis and correlation analysis as well as averaging method. The results showed that the vegetation coverage in the Qilian Mountains increased gradually from the west to the east,and it in the eastern part of the Qilian Mountains was higher than that in the western part. There were some significant spatial differences of vegetation coverage change,for example,it increased in the central and western parts of the Qilian Mountains,but decreased in the eastern part. The area with an increase of vegetation coverage was 79 149 km2and accounted for 52. 93% of the total area investigated in the Qilian Mountains,and the area with a decrease of that was 22 865 km2and accounted for 11. 09% of the total. The vegetation coverage was in a significant increase trend in recent 10 years,and the monthly vegetation coverage in growth season was also in an increase trend. The increase of vegetation coverage in the Qilian Mountains was caused by the increase of precipitation under global warming. There were the significant correlations between NDVI and temperature and precipitation,and there was also a time lag in their changes. There was a significant correlation between the NDVI in June and July and the precipitation in previous one month and two months,and their correlation coefficients were 0. 788 and 0.684,respectively; there was an extremely significant correlation between the NDVI in August and September and the temperature in the same month and previous one month,and their correlation coefficients were 0. 825 and 0.829,respectively.
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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 | 0.000 |
| 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".