Bibliographic record
Abstract
Human experience with snow avalanches has motivated pursuits to determine the physics underlying the motion of flowing snow. Of particular practical concern is the behavior of extreme avalanche runout, with important implications for infrastructure defense and hazard mapping in mountainous terrain. Both probabilistic extreme runout analyses, based on terrain parameters and statistical methods, and dynamic models, using a variety of flow laws, have been introduced and refined with varying success. The current investigation employs a numerical solution of the hydrodynamic equations for unsteady, open channel hydraulics using quasi-two-dimensional formulations of continuity and momentum conservation. In contrast to a fixed-grid (Eulerian) coordinate system as employed in most preceding models, a moving (Lagrangian) reference frame, following the flow downslope, is adopted. This is believed to be more appropriate given the unsteady nature of avalanche flow. Due to the uncertainty surrounding the dynamics of basal snow entrainment, and the certain physical importance of this process in the upper reaches of the avalanche path, the model uses the middle of the avalanche track as the starting point for the numerical simulation. Below this point, entrainment and other resistive terms are presumed negligible compared to basal friction, which becomes the dominant parameter influencing the runout distance. An empirical upper limit envelope for maximum avalanche speed, as a function of total vertical fall of the path, provides an initial velocity for the flowing mass. The initial volume or mass of the flow is prescribed based on the area of the starting zone and an initial slab thickness. The flow mass is divided into discrete, deformable blocks that maintain constant volume. Under passive internal pressure, the flow advances downslope to rest. Corresponding author address: Chris Borstad Department of Civil Engineering University of British Columbia Vancouver, B.C. V6T 1Z4 Canada
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".