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Record W3175857390 · doi:10.5194/egusphere-egu21-16565

Dynamic fracture mechanics in dry snow slab avalanche release

2021· article· en· W3175857390 on OpenAlexaff
D. M. McClung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSlabUltimate tensile strengthSnowMaterials scienceFracture (geology)Fracture mechanicsBrittlenessShear (geology)Composite materialMechanicsGeologyPhysicsGeophysicsMeteorology

Abstract

fetched live from OpenAlex

Field observations and measurements show that dry snow slab avalanches initiate by propagating shear fractures within a relatively thin weak layer sandwiched between a planar, stronger, thicker slab above and stronger material below. After initiation, the weak layer fracture can propagate up and down slope for distances which range from about 10 to 100’s of meters to cause tensile fracture through the body of the slab which results in avalanche release. In this paper, dynamic fracture mechanics is applied to slab tensile fracture after which avalanche release is imminent. Two mechanisms for production of tensile stress are explored employing field measurements of slab properties and lab measurements. The first considers inertial effects related to quasi-brittle fracture near the tip of a propagating weak layer shear fracture. The second is concerned with the tensile stress generation from the stress drop behind the crack front as the fracture propagates in the weak layer. Analysis suggest that both mechanisms can contribute to produce the tensile fracture line which precedes avalanche release. Even though both mechanisms may operate together, they are analyzed separately in this paper.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.209
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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