Global Diagnostics of Ionospheric Absorption During X‐Ray Solar Flares Based on 8‐ to 20‐MHz Noise Measured by Over‐the‐Horizon Radars
Bibliographic record
Abstract
Abstract An analysis of noise attenuation during 80 solar flares between 2013 and 2017 was carried out at frequencies 8–20 MHz using 34 Super Dual Auroral Radar Network radars and the EKB ISTP SB RAS radar. The attenuation was determined on the basis of noise measurements performed by the radars during the intervals between transmitting periods. The location of the primary contributing ground sources of noise was found by consideration of the propagation paths of radar backscatter from the ground. The elevation angle for the ground echoes was determined through a new empirical model. It was used to determine the paths of the noise and the location of its source. The method was particularly well suited for daytime situations, which had to be limited for the most part to only two crossings through the D region. Knowing the radio path was used to determine an equivalent vertical propagation attenuation factor. The change in the noise during solar flares was correlated with solar radiation lines measured by GOES/XRS, GOES/EUVS, SDO/AIA, SDO/EVE, SOHO/SEM, and PROBA2/LYRA instruments. Radiation in the 1 to 8 Å and near 100 Å are shown to be primarily responsible for the increase in the radionoise absorption, and by inference, for an increase in the D and E region density. The data are also shown to be consistent with a radar frequency dependence having a power law with an exponent of −1.6. This study shows that a new data set can be made available to study D and E regions.
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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 | 0.000 |
| 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) | 0.000 | 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".