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Record W2036222581 · doi:10.1029/2006gl028238

Fracture energy applicable to dry snow slab avalanche release

2007· article· en· W2036222581 on OpenAlexafffund
D. M. McClung

Bibliographic record

VenueGeophysical Research Letters · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsSlabSnowFracture (geology)GeologyUltimate tensile strengthGeotechnical engineeringMaterials scienceComposite materialGeophysicsGeomorphology

Abstract

fetched live from OpenAlex

Dry slab avalanches release by a sequence of propagating fractures: first by shear fracture (mode II and mode III) in a weak layer at the base of the slab and second by tensile fracture through the crown after which release of the slab is imminent. The fracture energy is the energy which must be provided to produce a unit area of fracture surface. It is a key parameter in determining how and when fractures will propagate and important slab characteristics such as length and width that are necessary in order to estimate slab mass and volume in relation to destructive potential. In this paper, approximate estimates of fracture energy are calculated from field measurements of slab properties, laboratory measurements and in‐situ strength tests in alpine snow. Estimates of fracture energy are given for both tensile fracture (mode I) through the crown as well as for shear fracture (mode II) in fragile weak layers of slab avalanches. The results, when compared with other values from ice, rock and engineering materials, suggest that snow has the lowest values.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.011
GPT teacher head0.277
Teacher spread0.266 · 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 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

Citations31
Published2007
Admission routes2
Has abstractyes

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