Application of Total Electron Content Derived from the Global Navigation Satellite System for Detecting Earthquake Precursors
Why this work is in the frame
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Bibliographic record
Abstract
The time series of the total electron content (TEC) at a certain location derived by the local network of ground-based Global Navigation Satellite System (GNSS) receivers or extracted from the global ionosphere map (GIM) is useful for detecting seismo-ionospheric anomalies at the regional level. When the detected anomalies are similar to those repeatedly appearing before large earthquakes in the same region, it might be perceived as a temporal seismo-ionospheric precursor (SIP). To discriminate the possible SIPs from global effects (such as solar disturbances, magnetic storms, etc.), a global search for anomalies using GIM TEC data is an ideal approach. Spatial analysis simultaneously detects anomalies similar to the temporal SIP at each lattice and indicates the global distribution or pattern of the detected anomalies. When the detected anomalies specifically and continuously appear within the monitoring region, we can observe spatial SIPs of the GIM TEC. To further study the fine structure and dynamics of the observed SIPs, a dense network of ground-based GNSS receivers is required. By applying the residual minimization training neural network (RMTNN) tomographic approach to the TEC between the GNSS satellite and the network receivers, the three-dimensional fine structure of the ionospheric electron density can be obtained.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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