A New Landslide Runout Model and Implications for Understanding Post Wildfire and Earthquake Threats to Communities in California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it