Bibliographic record
Abstract
Regional moment tensors (RMTs) provide important information for seismotectonic and hazard studies in regions with low to moderate seismicity, where infrequent earthquakes of Mw ≥ ∼4.0–4.5 occur that are too small for global momenttensor techniques. Moment-tensor analysis involves fitting theoretical waveforms with observed broadband waveforms and inverting for the moment-tensor elements ( e.g. , Aki and Richards 1980; Jost and Herrmann 1989). One powerful tool to calculate RMTs is the time domain surface wave waveform inversion code TDMT_INVC (Dreger and Helmberger 1993; Pasyanos et al. 1996; Dreger 2003). In recent years RMTs have been routinely calculated with this software in many parts of the world such as western Canada (Ristau et al. 2003, 2007), California (Dreger and Helmberger 1993; Romanowicz et al. 1993; Pasyanos et al. 1996), Alaska (Ratchkovski and Hansen 2002), Japan (Kubo et al. 2002), Taiwan (Kao et al. 1998), the European–Mediterranean region (Bernardi et al. 2004), and New Zealand (Ristau 2008). Only a few moment tensors/focal mechanisms are available for South Africa. This is due to moderate tectonic and deep mine-related seismicity, as well as, until recently, a sparse distribution of broadband seismometers in the South African National Seismograph Network (SANSN) (Saunders et al. 2008). A unique opportunity presented itself when the dense, very broadband Incorporated Research Institutions in Seismology (IRIS) PASSCAL Kaapvaal craton array was deployed in South Africa ( e.g. , Nguuri et al. 2001). From this array we identified three near regional Mw ∼4.0 earthquakes with suitable waveform data to calculate RMTs with the TDMT_INVC software. Our goal is to determine the moment magnitude, earthquake mechanism, and focal depth in order to 1) make progress in resolving the difference between local and moment magnitudes routinely determined with the SANSN; and 2) expand our understanding of the regional …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".