A Virtual Reality Simulation of a Real Landslide for Education and Training: Case of Chiradzulu, Malawi, 2023 Landslide
Bibliographic record
Abstract
Virtual reality (VR) is a promising new educational and training tool in the field of disaster and emergency management, especially for hazards that are not frequent or well known to the public and require spatial and situational understanding. The objective of this paper is to describe an educational VR simulation that was developed based on a landslide that really occurred in Southern Malawi during the March 2023 Cyclone Freddy. The cyclone induced several landslides that caused many casualties and significant damage. The VR simulation framework consisted of four critical steps using Unity3D for the creation of the simulation including data preparation, terrain and environmental modeling, landslide simulation development, and virtual reality interactions. The simulation scenarios were diversified to include three distinct landscapes that can help users learn how factors such as terrain can influence landslide impacts. The VR simulation offers users an intimate, firsthand experience of the landslide’s unfolding and allows users the ability to explore various facets of the landslide phenomena. This VR simulation aims to provide an educational tool to facilitate an in-depth understanding of and interaction with a real-word landslide to learn about the impacts of landslides and how different factors can influence these impacts.
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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 | 0.001 | 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 | 0.000 |
| 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".