Distributed simulations of landslides for different rainfall conditions
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Bibliographic record
Abstract
Abstract A physically based distributed slope stability model is described that utilizes a combined surface–subsurface kinematic wave module to assess groundwater fluctuations related to slope stability. A total of 82 major rainstorms from 1972 to 1990 in Carnation Creek, British Columbia, were examined to determine the influence of different characteristics of rainstorms (such as mean and maximum hourly intensity, duration, and rainfall amount) on the slope stability. These rainstorms vary in mean intensity from 1·6 to 11·2 mm h −1 , storm duration from 11 to 93 h, and maximum hourly intensity from 3·4 to 35 mm h −1 . Four synthetic ‘uniform intensity’ rainstorms were also tested against real storms to assess the effect of short‐term hourly rainfall intensity peaks on landslide occurrence. Altogether, 602 simulations were conducted. The combined influence of mean and maximum hourly intensity, duration, and total rainfall amount of rainstorms were important in generating landslides. The temporal distribution of short‐term intensity also influenced the landslide occurrence. When saturated hydraulic conductivity of the soil was lowered or soil depth was raised, most rainstorms produced larger numbers of landslides. For the most part, actual rainstorms produced less stable conditions than their synthetic ‘uniform intensity’ counterparts. For all landslide‐producing storms, slope failure usually occurred after some threshold of cumulative rainfall and maximum hourly rainfall intensity. These simulations provide insights into the distributed behaviour of landslide occurrence during large rainstorms with varying characteristics. Copyright © 2004 John Wiley & Sons, Ltd.
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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