CONSIDERATIONS ON GEOMAGNETIC DATA ANALYSIS
Bibliographic record
Abstract
This article aims to make a few remarks, examples and results about methods for analysis of geomagnetic data. In our study we used several methods and algorithms about numerical derivatives depending on time, polynomial regression, correlation factor, spectral analysis and wavelet analysis. With these algorithms were developed several programs in MatLab and AutoSignal software to study geomagnetic field morphology and to determine the spectrum of geomagnetic phenomena in different time intervals. Considering some period of time geomagnetic data recorded at the Observatory Surlari (colatitude 45.3°, longitude 26.3°) and comparing them with data recorded at other observatories, such as Ottawa (colatitude 44.6°, longitude 284.5°), Canberra (colatitude 125.3°, longitude 149.3°), Kakioka (colatitude 53.8°, longitude 140.2°) and Vernadsky (colatitude 155.3°, longitude 295.7°), including comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. From geomagnetic data analysis (spectral and wavelet) on definitive data in year 2007 recorded at two observatories to show that disadvantages of applying only the spectral analysis are related to less capacity of locating frequencies, amplitudes and phases in time. Also, the advantages and disadvantages of Fourier Transform and Wavelet Transform are discussed.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".