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Record W4309825716 · doi:10.1029/2022jf006622

Influence of Pore Fluid on Grain‐Scale Interactions and Mobility of Granular Flows of Differing Volume

2022· article· en· W4309825716 on OpenAlexafffund
Alexander Taylor-Noonan, Elisabeth T. Bowman, Brian W. McArdell, Roland Kaitna, Jim McElwaine, W. Andy Take

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaLeverhulme Trust
KeywordsFlumeVolume (thermodynamics)Materials scienceDispersityMechanicsGranular materialVolume fractionFlow (mathematics)Fluid dynamicsMineralogyGeologyThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Abstract The presence of a pore fluid is recognized to significantly increase the mobility of saturated over dry granular flows. However, the mechanisms through which pore fluid increases mobility may not be captured in experimental flows of small volume typical of laboratory conditions. Here we present the results of dry and initially fluid saturated or “wet” experimental flows of near‐monodisperse coarse‐grained ceramic particles in a large laboratory flume for five source volumes of 0.2–1.0 m 3 . Measurements include flow height, velocity profile, pore pressure, and evolving solid volume fraction, as well as the final deposit shape. The dry experiments constrain the frictional properties of the common granular material and comparison with wet flows permits an independent evaluation of the interstitial fluid effects. These results demonstrate that flow dilation and strong variation in the velocity profile are directly linked to a greatly increased mobility for wet granular flows compared to dry, and a significant influence of scale as controlled by source volume on flow behavior. Excess pore pressure need not be present for these effects to occur.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.382

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

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