Calibration of Absolute Ground Motion Scaling for the Central Mississippi Valley using ANSS Digital Data
Bibliographic record
Abstract
This effort focuses on assembling an extensive data set of digital recordings of small earthquakes that occurred and were recorded in southeastern Canada and the New Madrid region of the central United States. A data set of over 15000 waveforms was assembled for this comparison. Rather than develop a new ground motion scaling model, the data sets are compared to the Atkinson and Boore (1995) and Atkinson (2004) models for eastern North America. Using moment magnitudes determined under current and previous USGS support, these models can be evaluated in an absolute sense. The Atkinson and Boore (1995) model is preferred for southeastern Canada. For the New Madrid region neither characterizes derived ground motion scaling with distance, although the Atkinson and Boore (1995) does better in predictin g the scaling of motions with earthquake moment magnitude. 1. Comparisons of the Southeastern Canada and New Madrid Data sets This report consists of three sections and an Appendix: A summary comparison of the regression results from the data sets followed by a detailed discussion of the New Madrid and Southeastern Canada data sets. For the central U. S., the data sets were generated by the seismic networks sponsored by the USGS and USNRC and operated by Saing Louis University and CERI at the University of Memphis. The central U. S. data set was assembled by Mohammed Samiezade-Yazd, Luca Malagnini and Julia Kurpan during their tenure at Saint Louis University. The southeastern Canada data set was put together by
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".