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Record W2320197251 · doi:10.3189/2012jog11j009

Deformation and energy of dry snow slabs prior to fracture propagation

2012· article· en· W2320197251 on OpenAlexafffund
D. M. McClung, Chris Borstad

Bibliographic record

VenueJournal of Glaciology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsSlabGeologyBendingDeformation (meteorology)Fracture (geology)Shear (geology)SnowGeotechnical engineeringMechanicsMaterials scienceComposite materialGeophysicsPhysicsPetrology

Abstract

fetched live from OpenAlex

Abstract Snow slab avalanches primarily release by propagation of shear fractures within thin weak layers under thicker slabs. The weak layer is typically on the order of 1 mm thick and fails in mode II. In some cases, the weak layer is thicker and there may be a need to consider slope-normal deformation as part of the energy condition prior to rapid propagation. In this paper, field measurements from shear fracture initiation and high-speed films are combined to consider the effects of slope-normal deformation on bending of the slab prior to propagation and its relation to the propagation condition. Slab bending is modelled using two limiting cases: (1) a uniformly loaded beam supported by a deforming weak layer, analogous to a Winkler foundation, and (2) a uniformly loaded unsupported cantilevered beam. The experimental and analytical results suggest that slab bending prior to fracture initiation is small or negligible. Two previous approaches to modelling slab avalanche initiation involving slab bending are discussed. Both models proposed strong slab-bending effects prior to initiation, which conflicts with our results. Our field observations and modelling both show that strong bending is a dynamic effect following slope-parallel weak-layer fracture initiation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2012
Admission routes2
Has abstractyes

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