A Multi-Disciplinary Approach to the Study of Large Rock Avalanches Combining Remote Sensing, GIS and Field Surveys: The Case of the Scanno Landslide, Italy
Bibliographic record
Abstract
This research aims to highlight the importance of adopting a multi-disciplinary approach to understanding the factors controlling large rock avalanches using the Scanno landslide, Italy, as a case study. The study area is the Mount Genzana, Abruzzi Central Apennines, characterized by the regional Difesa-Mount Genzana-Vallone delle Masserie fault zone. The Scanno landslide is famous for its role in the formation of the Scanno Lake. The landslide is characterized by a wide exposed scar, which was interpreted in previous studies as the intersection of high-angle joints and an outcropping bedding plane on which the landslide failed sometime between the Upper Pleistocene and the Holocene. In this study, the Scanno landslide was investigated through the integration of geological, geomechanical and geomorphological surveys. Remote sensing techniques were used to enrich the conventionally gathered datasets, while Geographic Information Systems (GIS) were used to integrate, manage and investigate the data. The results of the authors investigation show that the outcropping landslide scar can be interpreted as a low-angle fault, associated with the Difesa-Mount Genzana-Vallone delle Masserie fault zone, which differs from previous investigations and interpretations of the area. The low-angle fault provides the basal failure surface of the landslide, with two systematic high-angle joint sets acting as lateral release and back scarp surfaces, respectively. In light of these new findings, pre- and post-failure models of the area have been created. The models were generated in GIS by combining LiDAR (Light Detection and Ranging) and geophysics data acquired on the landslide body and through bathymetric survey data of the Scanno Lake. Using the pre- and post-failure models it was possible to estimate the approximate volume of the landslide. Finally, back-analyses using static and dynamic limit equilibrium methods is also used to show the possible influence of medium-to-high magnitude seismic events in triggering the Scanno landslide.
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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 | 0.002 | 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 | 0.001 |
| 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".