An exploration of potentially reversible controls on millennial-scale variations in the slip rate of seismogenic faults: Linking structural observations with variable earthquake recurrence patterns
Bibliographic record
Abstract
Paleoseismic studies show that faults within a fault system may trade off slip over time, with mechanically complementary faults displaying alternating fast- and slow periods. Each of these periods spans multiple seismic cycles, and typically involves ~20-25m of slip. This suggests that the relative strength (or tendency to slip) of individual faults varies, over time and displacement scales larger than those of individual seismic cycles. The mechanisms responsible for these strength variations must: affect rocks in the strongest portion of the fault (the brittle-ductile transition) as this likely controls the overall slip rate of the fault; be reversible (or able to be counteracted) on a cyclical basis; provide a negative feedback that operates to change the fault from its current state; and have a measurable effect on fault strength over a time or length scale that corresponds to the observed fast and slow periods of fault slip. In this paper, we systematically explore 19 potentially weakening and 11 potential strengthening mechanisms and evaluate them in light of these criteria. This analysis reveals a relatively small subset of mechanisms that could account for the observed behavior, leading us to suggest a possible model for fault strength evolution.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".