Earthquake and tsunami forecasts: Relation of slow slip events to subsequent earthquake rupture
Bibliographic record
Abstract
The 5 September 2012 M(w) 7.6 earthquake on the Costa Rica subduction plate boundary followed a 62-y interseismic period. High-precision GPS recorded numerous slow slip events (SSEs) in the decade leading up to the earthquake, both up-dip and down-dip of seismic rupture. Deeper SSEs were larger than shallower ones and, if characteristic of the interseismic period, release most locking down-dip of the earthquake, limiting down-dip rupture and earthquake magnitude. Shallower SSEs were smaller, accounting for some but not all interseismic locking. One SSE occurred several months before the earthquake, but changes in Mohr-Coulomb failure stress were probably too small to trigger the earthquake. Because many SSEs have occurred without subsequent rupture, their individual predictive value is limited, but taken together they released a significant amount of accumulated interseismic strain before the earthquake, effectively defining the area of subsequent seismic rupture (rupture did not occur where slow slip was common). Because earthquake magnitude depends on rupture area, this has important implications for earthquake hazard assessment. Specifically, if this behavior is representative of future earthquake cycles and other subduction zones, it implies that monitoring SSEs, including shallow up-dip events that lie offshore, could lead to accurate forecasts of earthquake magnitude and tsunami potential.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".