On the spatial distribution of decameter‒scale subauroral ionospheric irregularities observed by SuperDARN radars
Bibliographic record
Abstract
Abstract The midlatitude Super Dual Auroral Radar Network (SuperDARN) radars regularly observe nighttime low‒velocity Sub‒Auroral Ionospheric Scatter (SAIS) from decameter‒scale ionospheric density irregularities during quiet geomagnetic conditions. To establish the origin of the density irregularities responsible for low‒velocity SAIS, it is necessary to distinguish between the effects of high frequency (HF) propagation and irregularity occurrence itself on the observed backscatter distribution. We compare range, azimuth, and elevation data from the Blackstone SuperDARN radar with modeling results from ray tracing coupled with the International Reference Ionosphere assuming a uniform irregularity distribution. The observed and modeled distributions are shown to be very similar. The spatial distribution of backscattering is consistent with the requirement that HF rays propagate nearly perpendicular to the geomagnetic field lines (aspect angle ≤1°). For the first time, the irregularities responsible for low‒velocity SAIS are determined to extend between 200 and 300 km altitude, validating previous assumptions that low‒velocity SAIS is an F‒region phenomenon. We find that the limited spatial extent of this category of ionospheric backscatter within SuperDARN radars' fields‒of‒view is a consequence of HF propagation effects and the finite vertical extent of the scattering irregularities. We conclude that the density irregularities responsible for low‒velocity SAIS are widely distributed horizontally within the midlatitude ionosphere but are confined to the bottom‒side F‒region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".