Statistical Analysis of Pre‐earthquake Electromagnetic Anomalies in the ULF Range
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Assessing the statistical significance of electromagnetic anomalies in the ultralow frequency (ULF) range observed prior to earthquakes is a necessary step toward determining whether these perturbations constitute actual earthquake precursors. A statistical epoch analysis (SEA) was recently performed by Han et al. (2014, https://doi.org/10.1002/2014JA019789 ) to analyze earthquakes happening between 2001 and 2010 near the geomagnetic observatory of Kakioka, Japan; the authors found a significant number of anomalies 6 to 15 days prior to the earthquake day within 100 km from Kakioka, while no significant pre‐earthquake activity was observed for the farther region 100 to 216 km from the observatory. In this current paper, we describe the application of our independent software implementation of their method. Despite using a different outlier rejection scheme, we manage to approximate their results. Upon validation of our program, we conduct multiple sensitivity studies. First, we explore how different outlier rejection schemes impact the results. We then restrict the analysis to only mantle earthquakes, highlighting a marginally significant number of anomalies prior to the earthquake day. Next, we test a higher band‐pass filter than the one initially used but find no anomalous pre‐earthquake activity in this higher‐frequency band. We then use a different catalog to establish the list of qualifying “earthquake days” which also leads the anomalous pre‐earthquake episode to vanish, thus raising concerns about the robustness of the results. Finally, we apply the SEA to another time window, ranging from 2013 to 2018: No significant pre‐earthquake episode can be observed for this interval. We conclude our study by providing guidelines for upcoming work.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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