Monitoring in Dynamic Change of Desertification in the Region of Central Asia Based on NOAA/AVHRR Image
Bibliographic record
Abstract
There are rather large parts of deserts and semi deserts in central Asia, belonging to the region of drought and semi-drought, with little annual precipitation and sharp change of daily and seasonal temperature change. This region is the concerning ecological fragile region in the study of global environmental change. The paper analyzed the landscape characteristics of desertification and soil desertification in the study area, as well as their relationship Based on the high-resolution TM image. And then, the 1km spatial resolution NOAA/AVHRR image was used to acquire the normalized difference vegetation index (NDVI). The vegetation coverage was estimated to ensure the monitoring index of desertification. The NDVI graph was overlapped with the image to set up the interpretation relationship between the ground objects and remote sensing features, on the basis of the relevant technique standards of desertification monitoring, its classification method, as well as the actual conditions. From the desertification interpretation, the area and degree of desertification could be monitored to analyze the changing trend of desertification in central Asia from 1989 to 2009. The results showed that the monitoring method used in the paper was with high reliability and practicality, which could do quick, macroscopic and timely monitoring of desertification in the region of central Asia.
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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) | 0.000 | 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 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".