Bibliographic record
Abstract
Abstract. Numerical models of earthquake rupture are used to investigate characteristic length scales and size distributions of repeated earthquakes on vertical, planar fault segments. The models are based on exact solutions of static three-dimensional (3-D) elasticity. Dynamical rupture is approximated by allowing the static stress field to expand from slip motions at a single velocity. To show how the vertical fault width affects earthquake size distributions for a broad range of fault behaviors, two different fault strength models are used; a smooth model and a heterogeneous asperity model. The smooth model is a simplified version of the Dieterich-Ruina rate and state dependent friction law. The heterogeneous asperity model uses a slip-dependent random powerlaw strength distribution. It is shown that the characteristic scale of fault segmentation is proportional to the vertical width of a seismogenic fault. This conclusion holds for both the smooth and the heterogeneous models. For the smooth models characteristic quake distributions result, with populations of large events that are obviously distinct from smaller events. The distributions of large events have well-defined mean lengths and moments. The heterogeneous models result in Gutenberg-Richter (GR) powerlaw distributions of event sizes up to a characteristic quake size. Quakes larger than the characteristic size fall off the GR distribution such that the powerlaw would greatly overestimate the probability of occurrence of the larger events.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".