On factors controlling precursor slip fronts in the laboratory and their relation to slow slip events in nature
Bibliographic record
Abstract
Abstract Spatial variations in frictional properties on natural faults are believed to be a factor influencing the presence of slow slip events (SSEs). This effect was tested on a laboratory frictional interface between two polymethyl methacrylate (PMMA) bodies. We studied the evolution of slip and slip rates that varied systematically based on the application of both high and low normal stress (σ0=0.8 or 0.4 MPa) and the far‐field loading rate (VLP). A spontaneous, frictional rupture expanded from the central, weaker, and more compliant section of the fault that had fewer asperities. Slow rupture propagated at speeds Vslow∼0.8 to 26 mm s−1 with slip rates from 0.01 to 0.2 μm s−1, resulting in stress drops around 100 kPa. During certain nucleation sequences, the fault experienced a partial stress drop, referred to as precursor detachment fronts in tribology. Only at the higher level of normal stress did these fronts exist, and the slip and slip rates mimicked the moment and moment release rates during the 2013–2014 Boso SSE in Japan. The laboratory detachment fronts showed rupture propagation speeds Vslow/VR∈ (5 to 172) × 10−7 and stress drops ∼ 100 kPa, which both scaled to the aforementioned SSE. Distributions of asperities, measured using a pressure sensitive film, increased in complexity with additional normal stress—an increase in normal stress caused added complexity by increasing both the mean size and standard deviation of asperity distributions, and this appeared to control the presence of the detachment front.
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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 | 0.001 |
| 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".