Comprehensive earthquake-induced landslide inventory dataset of the 2010 Chile megathrust earthquake
Bibliographic record
Abstract
The dataset was collected from bibliographic compilation, mapping by interpretation of Landsat satellite images, and field inspections; for the bibliographic compilation reviewed 107 technical reports of the National Geological and Mining Survey of Chile (SERNAGEOMIN) related to the Maule earthquake, from which the relevant information of landslides and lateral spreads was extracted. Also, the georeferenced reports of road network interruption problems caused by the Maule earthquake, undertaken by the Ministry of Public Works, were reviewed. Finally, it incorporated an inventory of lateral spreads provided by Verdugo (2012) and the inventory of landslides in the coastal fringe of the Biobio administrative region provided by Mardones and Rojas (2012). Additionally, coseismic landslides were mapped by using Google Earth to interpret Landsat satellite images (Landsat 5-7-8; provider, NASA; resolution, 30 m) before and after the earthquake. These strips were visually inspected at an eye height of ~ 1–2 km, decreasing the height when an alteration was detected in the vegetation or when bare spots or typical mass movement morphologies were present (Soeters and Van Western, 1996). ]. It was visually inspected, and the earliest available images after the earthquake were mapped at 1:2000 and 1:10,000. Once a landslide was identified, the location was compared with the latest preseismic image without cloud or snow cover and the landslide was mapped as a polygon. Field inspections were undertaken in the coastal regions, where the higher densities of landslides are located. The minimum size considered for the mapping was 30 m2, although field inspections showed that an indefinite number of small mass movements were not recognised on the satellite images.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.008 | 0.009 |
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".