Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran
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
Landslide is a natural hazard that causes many damages to the environment. Depending on the landform, several factors can cause the Landslide. This research addresses the methodology for landslide susceptibility mapping using multiple regression analysis and GIS tools. Based on the initial hypothesis, ten factors were recognized as effectual elements on landslide, which is geology, slope, aspect, distance from roads, faults and drainage network, soil capability, land use and rainfall. Crossing investigated parameters with the observed landslides indicated that three factor including distance from channel network, distance from fault and rainfall have no major effect on observed landslide in Tajan area. In order to quantifying the parameters in the form of weighting factors, the coverage of landslides in different observation was determined. Then Stepwise method was used for statistical analysis. It was found that slope, aspect, distance from the roads and soil capability are as most effective factors in landslide respectively.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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