STRATEGY ON THE LANDSLIDE TYPE ANALYSIS BASED ON THE EXPERT KNOWLEDGE AND THE QUANTITATIVE PREDICTION MODEL
Bibliographic record
Abstract
This paper discusses the applicability of analysis on the "landslide types" based on the quantitative prediction model for landslide hazard mapping. The quantitative prediction model used in this study construct the relationship between the past landslide occurrences and various kinds of geographical information termed "causal factors". One of the strong demands of the experts working on the landslide is to analyze the "different types of landslides", through the prediction models. Based on a previous study, it was decided to use a fuzzy-set theory model (using algebraic sum operator) for analysis among many integration tools. The analytical procedure was divided into the following two stages: • Comparison of the prediction maps produced by the prediction model, with respect to the various landslide types, such as scarp collapse, rotational landslide, translational landslide, flow and flowslide. • Comparison between the prediction maps and the hazard map made by the geomorphologist. In these analyses, two kinds of difference maps (termed DIF map-A and DIF map-B) were provided. The DIF map-A is made by the two prediction maps with respect to the different landslide types, while the DIF map-B is made by each prediction map and the hazard map produced by the geomorphologist. Based on the experiment for the Alpago region in Italy, it is indicated that the hazardous area affected by the different landslide types could be analyzed through the DIF map-A, furthermore, the DIF map-B taking account of the expert's opinion is effective to find out the hazardous area with respect to the landslide types.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".