A Simulation and Visualization Environment for Spatiotemporal Disaster Risk Assessments of Network Infrastructures
Bibliographic record
Abstract
Emerging methodologies for risk assessments of civil infrastructure networks require the coupling of several spatiotemporal models that need to be executed multiple times with varying parametrizations to account for model uncertainty and to investigate “what-if” scenarios. These requirements led to the development of a software environment to support the simulation process and the visual analysis of its results. The simulation engine component of the environment makes it possible to define, couple, and execute models. An embedded infrastructure model facilitates the development of functionality to estimate and aggregate capacity measures of single objects affected by multiple hazards. The simulation manager component can be used to execute multiple instances of the simulation engine conveniently with varying parametrizations. The included visualization tool provides two complementary views. The ensemble view can be used to analyze the data at a highly aggregated level with information visualization techniques and the simulation view can be used to investigate simulations in greater detail via an interactive map window and a state dependency graph. The software environment is used in a risk assessment for the region of Chur, Switzerland, which comprises the simulation of multiple natural hazard scenarios that lead to impaired transport infrastructure capacities and thus to disrupted traffic flows.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".