Quantitative Landslide Hazard Assessment Using Frequency Ratio Model: A Case Study from SMK Kundasang, Sabah, Malaysia
Bibliographic record
Abstract
The landslide issue is considerably prominent in the SMK Kundasang area. In 2012, the school was evacuated due to the instability of the ground on the premise. This study aims to research the landslide hazard in the affected area by employing a multi-hazard Frequency Ratio model. The first step was to create a landslide distribution map consisting of 191 landslides in total. 70% of it was used to generate the model. Meanwhile, the rest of it was utilized to verify the model. The ten landslide causative factors integrated to produce the model were lithology; soil series; distance from lineament, drainage, and road; slope angle; slope aspect; elevation; precipitation rate; and land use map. Area Under the Curve (AUC) analysis showed that the model performed at 84.0% of prediction accuracy. The hazard map was interpreted as having 12% very low, 23% low, 29% moderate, 25% high, and 12% of very high landslide hazard levels in the study area, respectively. The proposed approach in this study is deduced to be applicable to yield reasonable outcomes that benefit the development planning in SMK Kundasang.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".