Techniques to determine the quiet day curve for a long period of subionospheric VLF observations
Bibliographic record
Abstract
Abstract Very low frequency (VLF) transmissions propagating between the conducting Earth's surface and lower edge of the ionosphere have been used for decades to study the effect of space weather events on the upper atmosphere. The VLF response to these events can only be quantified by comparison of the observed signal to the estimated quiet time or undisturbed signal levels, known as the quiet day curve (QDC). A common QDC calculation approach for periods of investigation of up to several weeks is to use observations made on quiet days close to the days of interest. This approach is invalid when conditions are not quiet around the days of interest. Longer‐term QDCs have also been created from specifically identified quiet days within the period and knowledge of propagation characteristics. This approach is time consuming and can be subjective. We present three algorithmic techniques, which are based on either (1) a mean of previous days' observations, (2) principal component analysis, or (3) the fast Fourier transform (FFT), to calculate the QDC for a long‐period VLF data set without identification of specific quiet days as a basis. We demonstrate the effectiveness of the techniques at identifying the true QDCs of synthetic data sets created to mimic patterns seen in actual VLF data including responses to space weather events. We find that the most successful technique is to use a smoothing method, developed within the study, on the data set and then use the developed FFT algorithm. This technique is then applied to multiyear data sets of actual VLF observations.
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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 | 0.002 | 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) | 0.002 | 0.001 |
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".