Magnitude Clustering During Stick‐Slip Dynamics on Laboratory Faults
Bibliographic record
Abstract
Abstract We present an analysis of magnitude clustering of microfractures inferred from acoustic emissions (AEs) during stick‐slip (SS) dynamics of faulted Westerly granite samples in frictional sliding experiments, with and without fluids, under triaxial loading with constant displacement rate. We investigate magnitude clustering in time across periods during, preceding and after macroscopic slip events on laboratory faults. Our findings reveal that magnitude clustering exists such that subsequent AEs tend to have more similar magnitudes than expected. Yet, this clustering only exists during macroscopic slip events and is strongest during major slip events in fluid‐saturated and dry samples. We demonstrate that robust magnitude clustering arises from variations in frequency‐magnitude distributions of AE events during macroscopic slip events. These temporal variations indicate a prevalence of larger AE events right after (0.3–3 s) the SS onset. Hence, magnitude clustering is a consequence of non‐stationarities.
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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 | 0.001 | 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".