Cascadia subduction zone earthquake simulations for earthquake early warning testing
Bibliographic record
Abstract
This data package includes 112 simulated kinematic earthquake ruptures and associated waveforms for the Cascadia subduction zone (CSZ). The ruptures range in moment magnitude (M) 6.6–9.4. The waveforms were generated for 29 collocated GNSS and seismic stations onshore Vancouver Island, 5 ocean bottom seismometers in a cabled array offshore Vancouver Island, and 157 seismic stations onshore coastal Washington and Oregon. The CSZ stretches from northern California to southern British Columbia and is expected to generate a M9+ event or several M8+ events. Strong shaking from such events would likely be felt several hundred kilometers inland, which includes major cities such as Vancouver, Seattle, and Portland, thus making it a region of high seismic concern. However, this region experiences very little seismicity on a regular basis. This data package was created to supplement the dearth of observed regional earthquakes for use in earthquake early warning testing. The rupture files include information about the distribution and evolution of slip for each earthquake, and the waveforms include displacement and acceleration time series. These data were generated using a 1D semistochastic model, with displacement waveforms sampled at 2 Hz and acceleration waveforms sampled at 100 Hz. Two minutes of padding were prepended to the simulated waveforms prior to the addition of noise. Synthetic GNSS noise was used for the displacement waveforms, and real noise from several stations was used for the acceleration waveforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".