A New Landslide Runout Model and Implications for Understanding Post Wildfire and Earthquake Threats to Communities in California
Bibliographic record
Abstract
Wildfires and earthquakes contribute to a nearly ever-present cycle of hazards that are man-aged by coastal California communities every year. Worse still, fires, and earthquakes drive slope instability, primarily in the form of debris flows, debris avalanches, and debris floods whose runout can impact environment, infrastructure, and threaten lives along the landslide path. A better understanding of future landslide runout paths, travel distance, and potential landslide depth along the path, will improve our ability to manage future hazards; however, predictive models can be difficult to implement, hard to calibrate, and/or expensive to acquire. DebrisFlow Predictor is an agent-based runout model that predicts runout, inundation, scour, and deposition along the path, of debris flows and debris avalanches. Results credible and easily verified (numerically or visually) using several built-in features. DebrisFlow Predictor is intended to better inform and constrain land management decisions where debris flow and debris avalanche hazards exist.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".