Why this work is in the frame
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Bibliographic record
Abstract
BEAT is an open-source software tool for the robust characterization of the temporal and spatial evolution of earthquake rupture processes. It uses kinematic rupture models that include low-parametric models like Moment Tensors but also complex high-parametric, finite-extent sources. In other words, BEAT allows studying earthquakes on a first-order level as points with location, size and mechanisms. In consecutive steps, the complexity of the source model may be increased by various details up to the potential to resolve rupture dimension, fault segmentation, slip-distribution and slip-history. The source model parameters and their uncertainties are estimated based on seismic waveforms, and/or geodetic observations like InSAR and GNSS data. Rapid forward modeling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. Based on these, synthetic data are provided for arbitrary earthquake rupture models embedded in heterogeneous media. For an extensive exploration of the often high-dimensional model parameter space, BEAT offers a suite of sampling algorithms for high-standard Bayesian inference. The implementations of these sampling algorithms exploit the parallel architecture of modern computers for optimal performance. Finally, BEAT offers easy configuration and automatic visualization of relevant results. The software relies on functionality from PYROCKO (Heimann et al., 2017) and KITE (optionally, Isken et al., 2017).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it