Numerical Investigation of Statistical Properties of Microslips in Silos
Bibliographic record
Abstract
A planar problem of filling a silo with random in diameter disks is simulated numerically. The disks are placed statically one at a time, and each added disk results in a redistribution of normal and shear forces between the disks. It is well known that in this system microinstabilities involving local slips take place. The number of disks participating in a microslide is the result of the process of self organization, i.e., finding the stable state. As opposed to the previous research when the distribution of slide sizes (called microavalanches) was measured in a variable volume of particles, as reported by Bak et al. in 1988 and Claudin and Bouchaud in 1997, in this study the number of slips is measured and it is related to a constant volume (number of particles in the system). The statistic is collected by performing 600 fillings of the silo with the total number of particles 400, and recording the needed data after the 400th particle is dropped into the silo. The simulations are performed for the fixed coefficients of friction, distribution of particle sizes, and geometry of the boundary. The results of this study show the convergence of statistical properties for the number of slips and the energy lost to stable distributions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".