Measuring the mechanical properties of snow relevant for dry-snow slab avalanche release using particle tracking velocimetry
Bibliographic record
Abstract
Particle tracking velocimetry (PTV) is a measurement technique widely used to determine\ndisplacement and velocity fields from video recordings. It is largely nonintrusive and capable of simultaneously\nmeasuring the state of deformation over an entire cross section of a sample. PTV has been used\nin field and laboratory experiments since the mid-1990s to study snow deformation and fracture. Initial\nstudies focused primarily on documenting weak layer collapse and crack propagation velocities. However,\nrecent technological and computational advances allow researchers to determine essential mechanical\nproperties relevant to the processes involved in snow slab avalanche release. Indeed, PTV has been\nused to estimate the effective elastic modulus of the slab, weak layer specific fracture energy, crack\npropagation distance and speed, and the friction between the slab and the bed surface after fracture. In\nthis contribution, we will give an overview of over 500 field experiments performed in Canada, USA, and\nSwitzerland over the last 15 years, with an emphasis on relating derived snow mechanical properties to\ncommonly observed snow cover characteristics. For instance, our results suggest that crack propagation\nspeed, which increases with slab density, strongly correlates with crack propagation distance. Furthermore,\ncrack face friction, which determines the critical slope angle at which an avalanche releases, is affected\nby the hardness differences across the weak layer. While PTV has improved our understanding of\nthe fundamental processes involved in snow fracture, we will also highlight topics that have received little\nattention to date.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".