Downhill progressive landslide hazard assessment: a simple framework for prediction of triggering thresholds and failure modes
Bibliographic record
Abstract
The pronounced discrepancies in triggering intensities and failure modes observed in downhill progressive landslides pose significant challenges to their risk prediction and mitigation. Instead of focusing on an individual triggering event and/or failure mode as done in previous studies, this study systematically explores the instability issues of downhill progressive failure in long slopes. A unified analytical model is proposed to understand these observed discrepancies, in which criteria for the initiation of slope failure corresponding to all possible failure modes are formulated. Accordingly, a simple framework is established that is capable of predicting possible triggering thresholds and failure modes in advance. This would improve the accuracy of landslide hazard assessment, which is often reduced by the unknowns of the failure mechanisms and inaccurate assumptions of stress distribution along the failure surface. Furthermore, three practical cases are analyzed based on the established framework, and the findings are compared with field observations and existing studies, showing good consistency. This work gives an insight into a systematic understanding of the various failure phenomena in downhill progressive landslides and provides a reference for their risk assessment and subsequent decision-making.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".