QuickAware: a virtual reality tool for quick clay landslide hazard awareness
Bibliographic record
Abstract
Abstract Disaster emergency management is crucial for safeguarding lives and the environment in the face of natural and human-caused calamities, such as quick clay landslides. Disaster emergency management encompasses hazard identification, prevention, response, and recovery, most of which require knowledge and information acquired through training initiatives. Quick clay landslides pose a substantial hazard in regions like Scandinavia, Canada, Alaska, and Russia. With numerous historical incidents, knowledge-based awareness of quick clay landslide hazards is of paramount importance. In recent years, the area of application of virtual reality (VR) has grown tremendously from the entertainment industry to the military, to mental health, to hazard identification training and our daily lives. VR has been widely employed in hazard identification and prevention, safety training, evacuation, search and rescue, and damage identification of hazards. This study outlines the design, development, and implementation of QuickAware, a VR tool designed to create awareness of quick clay hazards. The development process of the tool started with a co-design approach where stakeholder experts were brought to collaborate in setting up VR scenarios and defining the VR environment contents. The contents were then conceptualized and translated into a VR experience. The novelty of the tool is that it immerses users in a realistic experience, allowing them to engage directly with the causes and consequences of quick clay landslide disasters which would be dangerous in real-life. The usability of the VR tool was examined by surveying 16 participants. The preliminary results of the survey indicated that the tool has a promising value in improving awareness creation for quick clay landslide hazards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".