ANALYSES OF GEOMAGNETIC DATA SETS FROM OBSERVATORIES AND CORRELATION BETWEEN THEM
Bibliographic record
Abstract
The purpose of this study was to analyze the associated spectrum of geomagnetic field, frequencies intensity and the time of occurrence. We calculated the variation of the correlation coefficients, with mobile windows of various sizes, for the recorded magnetic components at different latitudes and latitudes. We included in our study the observatories: Surlari (USA), Honolulu (HON), Scott Base (SBA), Kakioka (KAK), Tihany (THY), Uppsala (UPS), Wingst (WNG) and Yellowknife (YKC). We used the data of these observatories from INTERMAGNET for the bigest geomagnetic storm from the last two Solar Cycles. We have used for this purpose a series of filtering algorithms, spectral analysis and wavelet with different mother functions at different levels. In the paper, we show the Fourier and wavelet analysis of geomagnetic data recorded at different observatories regarding geomagnetic storms. Fourier analysis highlight predominant frequencies of magnetic field components. Wavelet analysis provides information about the frequency ranges of magnetic fields, which contain long time intervals for medium frequency information and short time intervals for highlight frequencies, details of the analyzed signals. Also, the wavelet analysis allows us to decompose geomagnetic signals in different waves. The analyzes presented are significant for the studied of the geomagnetic storm. The data for the next days after the storm showed a mitigation of the perturbations and a transition to a quiet day of the geomagnetic field. In both, the Fourier Transformation and the Wavelet Transformation, transformation evaluation involves the calculation of a scalar product between the analyzed signal and a set of signals that form a particular base in the vector space of the finite energy signals. Fourier representation use and orthogonal vectors base, whereas in the case of wavelet there is the possibility to use also bases consisting of independent linear non-orthogonal vectors. Unlike the Fourier transform, which depends only on a single parameter, wavelet transform type depends on two parameters, a and b. As a result, the graphical representation of the spectrum is different, wavelet analysis bringing more information about geomagnetic pattern of each observatory with that own specific conditions
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".