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Record W2109791932 · doi:10.1139/t2012-007

Physical models of rock avalanche spreading behaviour with dynamic fragmentation

2012· article· en· W2109791932 on OpenAlexaffvenue
Elisabeth T. Bowman, W. Andy Take, K.L. Rait, C.E. Hann

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeologyImpulse (physics)MechanicsGeotechnical engineeringBreakageRockfallDebrisBrittlenessProjectilePhysicsLandslideMaterials scienceClassical mechanics

Abstract

fetched live from OpenAlex

The dynamic fragmentation of rock during avalanche motion has been postulated as a mechanism explaining the long runout of large rock avalanches or Sturzströme. This paper investigates whether test conditions that produce dynamic fragmentation can lead to greater runout or spreading of physical model rock avalanches. Model avalanche experiments were carried out under enhanced acceleration to generate breakage in coal, a fragmentable, brittle solid. Coal blocks were released from a stationary position on a slope to run out on a plane. The motion of the ensuing fragmenting debris was captured using a high-speed camera placed above the horizontal plane. The average position of the front was tracked and the degree of fragmentation of the model avalanches was quantified. The paper presents results of the frontal velocity of the avalanches, corrected for centrifuge Coriolis effects. Comparison is made between the peak and impulsive front velocities, the final runout, and the degree of fragmentation of the model avalanches. Strong relationships are found between runout normalized by the cube root of volume, impulse velocity, and Hardin’s relative breakage parameter, B R . Results are discussed in light of the mechanics involved and are compared with field-scale events.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.729

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)0.0010.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations158
Published2012
Admission routes2
Has abstractyes

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