Integrating Gaming Technology to Map Avalanche Hazard
Bibliographic record
Abstract
As gaming physics engines advance, snow scientists have the opportunity to utilize these engines to animate and model avalanche flow and runout. Numerical models coupled with real world digital elevation model (DEM) data facilitate the understanding of avalanche movement in com- plex terrain. Combined with known avalanche runout datasets, genetic algorithms (GAs) determine appropriate physical parameters for dynamic models that can be applied to mountain ranges similar to the training datasets. The trained models provide avalanche path outlines to supplement hazard maps. Our approach simulates avalanche flow with a particle physics based emulation using parame- ters of initial height, bounce friction, stickiness, damping force, turbulence force, clumping factor, vis- cosity and cell grid size. The model requires a DEM and a starting zone approximation. Using a GA approach, a training dataset of 10 known avalanche paths in the Rogers Pass, Canada highway corri- dor with mapped maximum runout and associated starting zones was used to optimize the model pa- rameters to provide a best fit to the avalanche path outlines. The animated flow interface permits ad- justment or verification of parameters on the fly and visualization of the animation in 3D or stereo view if desired. The optimized model defines avalanche paths and runout and outputs path outlines in ras- ter format, which can be imported into a geographic information system (GIS). The raster outputs were integrated into an algorithm devised to map avalanche terrain exposure. Avalanche path activity, land cover data and topographic parameters such as slope and curvature contribute to identify hazardous terrain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".