Maximum Magnitude (<i>M</i><sub>max</sub>) in the Central and Eastern United States for the 2014 U.S. Geological Survey Hazard Model
Bibliographic record
Abstract
Probabilistic seismic‐hazard assessment (PSHA) requires an estimate of M max, the moment magnitude M of the largest earthquake that could occur within a specified area. Sparse seismicity hinders M max estimation in the central and eastern United States (CEUS) and tectonically similar regions worldwide (stable continental regions [SCRs]). A new global catalog of moderate‐to‐large SCR earthquakes is analyzed with minimal assumptions about enigmatic geologic controls on SCR M max. An earlier observation that SCR earthquakes of M 7.0 and larger occur in young (250–23 Ma) passive continental margins and associated rifts but not in cratons is not strongly supported by the new catalog. SCR earthquakes of M 7.5 and larger are slightly more numerous and reach slightly higher M in young passive margins and rifts than in cratons. However, overall histograms of M from young margins and rifts and from cratons are statistically indistinguishable. This conclusion is robust under uncertainties in M , the locations of SCR boundaries, and which of two available global SCR catalogs is used. The conclusion stems largely from recent findings that (1) large southeast Asian earthquakes once thought to be SCR were in actively deforming crust and (2) long escarpments in cratonic Australia were formed by prehistoric faulting. The 2014 seismic‐hazard model of the U.S. Geological Survey represents CEUS M max as four‐point probability distributions. The distributions have weighted averages of M 7.0 in cratons and M 7.4 in passive margins and rifts. These weighted averages are consistent with M max estimates of other SCR PSHAs of the CEUS, southeastern Canada, Australia, and India.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".