On the performance of CARISMA-Akademik Vernadsky station Schumann resonance monitoring
Bibliographic record
Abstract
The main objective of this study is to evaluate the effectiveness of the CARISMA (Canadian Array for Realtime Investigations of Magnetic Activity) – Akademik Vernadsky station (65.25°S 64.25°W, Vernadsky) Extremely Low Frequency (ELF) induction magnetometer network as a planetary monitoring system for thunderstorm activity, with observation sites located in the Arctic and Antarctic regions, respectively. To achieve this, daily ELF records from Vernadsky and Fort Churchill (FCHU, 58.76°N 94.08°W) collected in January 2022 were processed and analyzed. For CARISMA, data from the FCHU site were used due to the better signal-to-noise ratio. The horizontal magnetic components of Schumann signals obtained at Vernadsky and FCHU underwent spectral and polarization processing. ELF transients were identified, and subsequent geolocation was performed as well. Both regular (quiet) thunderstorm activity periods and an unprecedented local amplification of lightning activity near the Hunga Tonga-Hunga Ha'apai volcano during its eruption on January 15, 2022, were studied. Throughout the quiet periods, ELF signal processing yielded similar characteristics of integral lightning activity derived from CARISMA and Vernadsky records, consistent with findings in the literature and previous investigations at the Vernadsky site. On the other hand, the analysis of Schumann spectra and ELF transients during the Tonga volcano eruption confirmed that most thunderstorms were concentrated within a relatively small area around the epicenter, validating the point source model for the global lightning center. This paper demonstrates that the CARISMA and Vernadsky magnetometer network is well-suited for establishing a global lightning activity monitoring andintense lightning geolocation system. Such a system can be employed to assess and study global temperature trends, monitor the growth of lightning activity in high latitudes, and detect terrestrial, atmospheric, and geospace disaster phenomena.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".