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Record W2126540389

Measurements of weak snowpack layer friction

2009· article· en· W2126540389 on OpenAlexaboutno aff
Alec van Herwijnen, Joachim Heierli

Bibliographic record

VenueDORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research) · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowpackSlabSnowGeologyDynamical frictionCoefficient of frictionGeotechnical engineeringMechanicsSurface layerFracture (geology)Materials scienceCoulomb frictionLayer (electronics)Composite materialGeophysicsGeomorphologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

An essential stage in the release of an avalanche is the propagation of a fracture through a weak snowpack layer. As the fracture propagates, the slab looses its support and comes into contact with the bed surface. In the area of contact, the sliding motion of the slab is constrained by frictional forces. This particular friction process has received little experimental attention until today. Studies on sliding friction have mainly been performed on snow samples composed of small rounded grains in direct contact. In the present study we determine the friction coefficient of weak layers by analysing video records of snow samples sliding down-slope after a weak layer had fractured. The slab and the bed surface are then in indirect contact as the interface consists of the collapsed weak layer. The experiments were carried out in the mountains of British Columbia, Canada, and in the mountains around Davos, Switzerland , between 2002 and 2009. Assuming Coulomb-type friction the initial acceleration of the snow sample is used to determine the friction coefficients. The measurements show an initial friction coefficient on the order of 0.6 and the tendency to decrease thereafter. This is accompanied by the gradual erosion of the interface between the slab and the bed surface.

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)0.001
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 score0.106
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.130
GPT teacher head0.328
Teacher spread0.198 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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