Landslide runout: statistical analysis of physical characteristics and model parameters
Bibliographic record
Abstract
Landslides are treacherous, but risk management actions based on improved prediction of landslide runout can reduce casualties and damage. Forty rapid flow-like landslides of variable volume, entrainment, and composition are used to develop a volume-runout regression, which is compared to those established in previous research. The cases are analyzed to identify the most critical characteristics observable prior to failure which differentiate between events of high and low mobility. Mitigating long-runout flow-like landslides requires accurate hazard mapping, a task best accomplished through runout modelling. Current practice requires back-analyzing a set of cases consistent in scope with the target event, then applying the same rheology and parameters to forward modelling. This thesis determines which aspects of scope are most important to prioritize when selecting similar cases, as volume, movement type, morphology, and material have a more substantial influence on mobility than other physical characteristics. Statistical analysis of the performance of frictional and Voellmy rheologies over a range of parameters for the forty case studies provides the expected mean normalized runout and associated standard deviation, and recommendations for parameters to use in initial forward modelling of prospective events.
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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 | 0.000 | 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) | 0.000 | 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".