Economic Assessment of Landslide Risk for the Waidhofen a.d. Ybbs Region, Alpine Foreland, Lower Austria
Bibliographic record
Abstract
Presented at the 11th International Symposium on Landslides and the 2nd North American Symposium on Landslides and Engi- neered Slopes (NASL). Protecting Society through Improved Understanding. 2012 June 02-08. Poster. Canada, Banff (Alberta). This research aims to assess potential risk and estimate economic damage caused by landslides. The study area is located in the Ybbs valley, Lower Austria. Methodology consists of Arc GIS based spatial analysis and estimation of the potential monetary losses caused by landslides. Spatial analysis was used for defining elements at risk located in the risk zone of 100 m near landslides, and assessment of potential consequences. Results: calculated possible losses caused by the destruction of immobility and transport: costs for buildings demolition, restoration, roads rebuilding, debris transport. We defined risk zones. 1) Risk zone: 100-meter buffer area surrounding all landslides downwards, as a landslide with volume of 1 km3 may reach 100 meters while moving. 2) Elements at risk: The objects located in the 100 meter distance downwards the landslides are regarded as elements at risk. 3) Estimation of the economic losses: The methodology is proposed by Giacometti (2005) was applied for Ybbs valley using real estate prices for common goods (immobility, roads). Calculations of the economic losses (in €) was done based on the average real estate prices for various aspects of renovation, reconstruction and rebuilding of the destroyed objects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".