Bibliographic record
Abstract
Synthetic Aperture Radar Interferometry (InSAR) technology is an important developmental direction of microwave remote sensing in recent years, and now people are focused on researching its application of monitoring landslide both at home and abroad. In this paper, the principle of InSAR and Differential InSAR and the method of monitoring landslide are introduced firstly in brief. Then the study of its application and development in monitoring landslide are reviewed in detail. Many overseas countries have already carried on some application investigation and obtained better achievements, such as France, Italy, Canada, etc. But there are few application of using InSAR to monitor landslide at home. Comparing with traditional method, using of InSAR and DInSAR in monitoring landslide has many advantages. For example the images can be obtained every time when the SAR satellites pass the area, the InSAR images can cover a large area on the earth and the images have high-resolution and accuracy, etc. On the contrary, they will produce decorrelation in the course of practical application. In the last part, the developmental direction of InSAR in the future is explained. In order to carry on more effective monitoring in landslide, InSAR and PS technology should be utilized synthetically in monitoring landslide.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".