Comprehensive earthquake-induced landslide inventory dataset of the 2010 Chile megathrust earthquake
Bibliographic record
Abstract
Chile is one of the most seismically active countries on Earth and is often associated with cascading hazards, such as ground shaking, liquefaction, tsunamis, and coseismic landslides. Additionally, removal mass is a global hazard with devastating impacts resulting in thousands of fatalities every year, substantial economic losses, and long-term economic disruption. The dataset described in this article consists of a comprehensive landslide inventory for the 2010 Mw 8.8 Maule earthquake between 32.5° S and 38.5° S°. There are 1226 landslides over a mapped c.120,500 km 2 (1059 disrupted slides, 110 flows, 49 lateral spreads and eight coherent slides). The dataset was collected from bibliographic compilation, mapping by interpretation of Landsat satellite images, and field inspections. This data can be used as input in earthquake-induced landslide susceptibility models to help predict coseismic landslide occurrences after megathrust earthquakes and inform land use planning processes incorporating cascading hazard management and prevention.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".