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Discussion of “Description of Inherent and Induced Anisotropy in Granular Media with Particles of High Sphericity” by M. Oboudi, S. Pietruszczak, and A. G. Razaqpur

2017· article· en· W2765252645 on OpenAlexaboutno aff
Rui Wang, Pengcheng Fu, Zhaoxia Tong

Bibliographic record

VenueInternational Journal of Geomechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaEuropean CommissionU.S. Department of Energy
KeywordsSphericityAnisotropyGeotechnical engineeringGranular materialGeologyMaterials scienceStatistical physicsMechanicsMathematicsPhysicsGeometryOptics

Abstract

fetched live from OpenAlex

The discussers would like to compliment the authors from McMaster University for their continuous contribution to the study of the strength anisotropy of granular materials.The direct shear test data, the observed significant strength anisotropy of nearly spherical granular materials, and the mathematical formulation for strength anisotropy all provide valuable insights into this important subject.In this discussion, the discussers would like to raise three points, which they believe are complementary to the study under discussion in significant and constructive ways.First, the original paper covers only half of the complete range of the bedding-plane inclination angle, which describes the relationship between the loading direction and the deposition direction under the plane-strain condition.Second, the symmetrical form of the proposed shear strength function with respect to the deposition angle renders it incapable of capturing the complete dependence of granular material strength on shearing directions.Third, the discussers would like to refer the authors and readers of this paper to some recent results showing surprisingly high strength anisotropy in granular materials consisting of particles with even higher sphericity than that of Ottawa sand.These three points are elaborated in this discussion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.312

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)0.0000.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 designBench or experimental
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

Citations2
Published2017
Admission routes1
Has abstractyes

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