Methods for Evaluation of Geodetic Data and Seismicity Developed with Numerical Simulations: Review and Applications
Bibliographic record
Abstract
In this work we review the development of both established and innovative analytical techniques using numerical simulations of the southern California fault system and demonstrate the viability of these methods with examples using actual data. The ultimate goal of these methods is to better understand how the surface of the Earth is changing on both long-and short-term time scales, and to use the resulting information to learn about the internal processes in the underlying crust and to predict future changes in the deformation and stress field. Three examples of the analysis and visualization techniques are discussed in this paper and include the Karhunen-Loeve (KL) decomposition technique, local Ginsberg criteria (LGC) analysis, and phase dynamical probability change (PDPC). Examples of the potential results from these methods are provided through their application to data from the Southern California Integrated GPS Network (SCIGN), historic seismicity data, and simulated InSAR data, respectively. These analyses, coupled with advances in modeling and simulation, will provide the capability to track changes in deformation and stress through time, and to relate these to the development of space-time correlations and patterns.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".