Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non‐Urbanized Areas
Bibliographic record
Abstract
Abstract Although most landslides are precipitation‐triggered, a number of other complex conditions simultaneously predispose any given slope to failure, with the impact of urbanization posing particular scientific challenges. We use panel regression with fixed effects—which controls for observed and unobserved time‐variant and time‐invariant influences—to quantify the effect of precipitation accumulation on landslide concentration across the Pacific Coast region of the United States. We find that landslide hazard is most sensitive to precipitation variations in urbanized areas. This finding is robust across 1‐day, 10‐day, and 30‐day periods of precipitation accumulation, among individual Pacific Coast states, and when the analysis is confined to the San Francisco Bay Area (a subregion with both urban and rural areas). Our results corroborate existing hypotheses that urbanization increases landslide hazard, while demonstrating the importance of considering interactions with urbanization when predicting landslide hazard in the current climate, and under climate change scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".