Uniformly Processed Fourier Spectra Amplitude Database for Recently Compiled New Zealand Strong Ground Motions
Bibliographic record
Abstract
Abstract We present a ground-motion parameter database for earthquakes recorded between 2000 and the end of 2022 in New Zealand, which was developed within the New Zealand National Seismic Hazard Model (NZ NSHM 2022) program. It comprises all the local events with moment magnitudes in the range Mw 3.5–7.8 for crustal seismicity and Mw 4–7.8 for subduction seismicity recorded by GeoNet strong-motion network. Out of 2809 events, 1598 (∼57.1%) were classified as crustal, 432 as interface (∼15.3%), 98 as outer-rise (3.5%), 597 as inslab (∼21.3%), and the rest are undetermined. Beside the information that GeoNet provides for each event, the source metadata also comprises moment tensor solutions and finite-fault source models compiled from the literature. Various distance measures are computed for each event–station pair, including estimates of rupture distance for sufficiently large events by incorporating finite-fault source models. More than 150,000 strong ground-motion records, within 500 km rupture distance, were processed using an automated algorithm that combines traditional processing algorithms and machine learning. Several intensity measures (i.e., smoothed and down-sampled Fourier spectral amplitudes, Arias intensity, cumulative absolute velocity, and duration measures) of the processed ground motions are presented in the database. Finally, the database includes station site parameters sourced directly from the 2022 NSHM compilation of Wotherspoon et al. (2022, 2023).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.010 | 0.005 |
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".