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Record W1606767205 · doi:10.1002/esp.3372

The use of ballistic trajectory and granular flow models in predicting rockfall propagation

2012· article· en· W1606767205 on OpenAlexaff
Franck Bourrier, Luuk Dorren, Oldrich Hungr

Bibliographic record

VenueEarth Surface Processes and Landforms · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsRockfallGeologyRock mass classificationTrajectoryTerminologyMass movementComputer scienceGeotechnical engineeringLandslidePhysics

Abstract

fetched live from OpenAlex

ABSTRACT The term rockfall is often used ambiguously to describe various mass movement processes. Here we propose more precise terminology based on the physical nature of the moving mass, differentiating between two distinct types of rockfall: fragmental rockfall and rock mass fall. For both rockfall types, the current knowledge of the mechanisms controlling propagation of the mass movement are described, showing how these mechanisms can be simulated with different modelling approaches. However, we point out that almost no development has been realized concerning dynamic behaviour of the transitional processes between these two end‐member rockfall types. Some simplified means of dealing with these complications are suggested, but we emphasize that a considerable amount of fundamental methodological development remains necessary. Copyright © 2012 John Wiley & Sons, Ltd.

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 score0.388
Threshold uncertainty score0.177

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 communication0.0000.001
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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

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