Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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