Sizes of the largest possible earthquakes in the central and eastern United States— Summary of a workshop, September 8–9, 2008, Golden, Colorado
Bibliographic record
Abstract
Most probabilistic seismic-hazard assessments require an estimate of Mmax, the magnitude (M) of the largest earthquake that is thought possible within a specified area. In seismically active areas such as some plate boundaries, large earthquakes occur frequently enough that Mmax might have been observed directly during the historical period. In less active regions like most of the Central and Eastern United States and adjacent Canada, large earthquakes are much less frequent and generally Mmax must be estimated indirectly. The indirect-estimation methods are many, their results vary widely, and opinions differ as to which methods are valid. This lack of consensus about Mmax estimation increases the uncertainty of hazard assessments for planned nuclear power reactors and increases design and construction costs. Accordingly, the U.S. Geological Survey and the U.S. Nuclear Regulatory Commission held an open workshop on Mmax estimation in the Central and Eastern United States and adjacent Canada. The workshop was held on Monday and Tuesday, September 8 and 9, 2008, at the U.S. Geological Survey offices in Golden, Colorado. Thirty-five people attended. The workshop goals were to reach consensus on one or more of: (1) the relative merits of the various methods of Mmax estimation, (2) which methods are invalid, (3) which methods are promising but not yet ready for use, and (4) what research is needed to reach consensus on the values and relative importance of the individual estimation methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".