Comparative study of the possible lower ionospheric anomalies in very low frequency (VLF) signal during Honshu, 2011 and Nepal, 2015 earthquakes
Bibliographic record
Abstract
We present perturbations in very low frequency (VLF) signals received at Ionospheric & Earthquake Research Centre (IERC) (Lat. 22.50°N, Long. 87.48°E) during and prior to two earthquakes, one on 11 March 2011 at 11:16:24 LT (M = 9) in Honshu, Japan and another on 12 May 2015 at 12:35:19 LT (M = 7.3) in Kodari, Nepal. The VLF signal transmitted from JJI (22.2 kHz) in Japan (Lat. 32.05°N, Long. 131.51°E) showed strong shift in VLF-sunrise terminator times towards night-time few days prior to both the earthquakes. These two earthquakes took place near the transmitter JJI and receiver IERC respectively. We simulated the VLF sunrise terminator time shifts using the long wavelength propagation capability (LWPC) code. To effectively represent the D-region ionospheric variabilities, we assumed a mean dynamic perturbation over the path and presented them with a set of effective Wait’s parameters (βeff, heff′). We have successfully reproduced the temporal trend of the normalized VLF signal amplitude at VLF sunrise terminators for a few days around both the earthquakes. We used Wait’s exponential model for estimating the altitude profile of D-region electron density (Ne(h)) at VLF sunrise terminator times around both the earthquakes and quantified the changes of those Ne(h) profiles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".