Effects of Particle Size on Fault Gouge Frictional Characteristics and Associated Acoustic Emission
Bibliographic record
Abstract
Our experimental work was designed to explore the particle size effect of simulated fault gouge on slip characteristics by the conventional double‐direct shear friction configuration combined with acoustic emission (AE). The following conclusions were drawn: (1) smaller particles allow for an initially higher compaction rate at a higher speed and longer duration for force chain formation and destruction. The larger the particle size is, the higher the slipping displacement rate is; (2) the smaller the particle size is, the larger the friction coefficient is, and thus the higher the fault strength is. In addition, the larger the shear velocity is, the higher the fault strength is; (3) the smaller the particle size is, the higher the shear stress drop generated by the stick‐slip is, and the stronger the dynamic slip intensity for a stick‐slip period is; and (4) surface defects of forcing blocks possibly help to embed foregoing “stability” and “stable sliding” into the normal stick‐slip stage. Especially, the “stable sliding” is possibly related to formation of stubborn force chains. These findings may shed some insights into further clarification of slipping characteristics and discrimination of precursory signs of fault dynamic instability with different‐sized gouge particles.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".