PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis
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
Mitigating the impacts of landslides and planning resilient infrastructure necessitates assessing the exposure to this hazard through, for example, susceptibility mapping involving the spatial integration of various contributing factors. Here, we introduce PyLandslide, an open-source Python tool that leverages machine learning and sensitivity analysis to quantify the weights of various contributing factors, estimate the associated uncertainties, and generate susceptibility maps. We apply PyLandslide to the case of rainfall-triggered landslides in Italy driven by historical precipitation data (1981–2023) and nine climate projections for the mid-century (2041–2050). Results highlight distance to roads as the most influential factor in determining landslide susceptibility in Italy, followed by slope. Our findings reveal an overall reduction in susceptibility in the mid-century compared to the historical period; however, the directional changes vary spatially. Uncertainty analysis should play a central role in decision-making on landslides, where weights are intricately linked to investments.
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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 | 0.000 | 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) | 0.001 | 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 it