Recent Advances in the Spatial and Temporal Modeling of Shallow Landslides
Bibliographic record
Abstract
Abstract: Factors that control the stability of mountain slopes may be in a tenuous state of equilibrium that can easily be upset by timber harvesting, vegetation conversion, and road construction. Shallow, rapid landslides are the dominant erosion and sediment delivery mechanisms in much of the steep terrain worldwide, especially where rainfall is high. Deep and dense tree root systems often contribute the additional component of soil shear strength necessary to insure long term stability of steep slopes. Road and foot paths may redistribute water onto marginally stable hillsides or hollows, promoting slope failure. Here we present a distributed shallow landslide model that captures the temporal dynamics of imposed management scenarios at the catchment scale. This physically-based model incorporates a planar infinite slope analysis module (based on factor of safety analysis), a kinematic wave groundwater module, and a module for continuous temporal changes in root cohesion and vegetation surcharge. The distributed landslide model is integrated with GIS and a topographic analysis, which partitions the basin into vector-based stream tube elements. Recent developments include evaluations of complex timber harvesting scenarios and assessing the effects of rainfall characteristics on landslide potential. Examples are presented showing the application of this landslide model to temporal scenarios of vegetation management in steep catchments. Benefits of using longer forest harvesting rotations are shown in a steep catchment on Vancouver Island, British Columbia. Potential applications of the model are discussed in managed tropical catchments where forest conversion may increase landslide potential. A major drawback of such distributed models is that they require rather intensive data inputs and are thus difficult to apply in remote areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".