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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".