Magnetic aspect sensitivity of high‐latitude <i>E</i> region irregularities measured by the RAX‐2 CubeSat
Bibliographic record
Abstract
The second Radio Aurora Explorer (RAX‐2) satellite has completed more than 30 conjunction experiments with the Advanced Modular Incoherent Scatter Radar chain of incoherent scatter radars in Alaska and Resolute Bay, Canada. Coherent radar echoing occurred during four of the passes: three when E region electron drifts exceeded the ion acoustic speed threshold and one during HF heating of the ionosphere by the High Frequency Active Auroral Research Program heater. In this paper, we present the results for the first three passes associated with backscatter from natural irregularities. We analyze, in detail, the largest drift case because the plasma turbulence was the most intense and because the corresponding ground‐to‐space bistatic scattering geometry was the most favorable for magnetic aspect sensitivity analysis. A set of data analysis procedures including interference removal, autocorrelation analysis, and the application of a radar beam deconvolution algorithm mapped the distribution of E region backscatter with 3 km resolution in altitude and ∼0.1° in magnetic aspect angle. To our knowledge, these are the highest resolution altitude‐resolved magnetic aspect sensitivity measurements made at UHF frequencies in the auroral region. In this paper, we show that despite the large electron drift speed of ∼1500 m/s, the magnetic aspect sensitivity of submeter scale irregularities is much higher than previously reported. The root‐mean‐square of the aspect angle distribution varied monotonically between 0.5° and 0.1° for the altitude range 100–110 km. Findings from this single but compelling event suggest that submeter scale waves propagating at larger angles from the main E × B flow direction (secondary waves) have parallel electric fields that are too small to contribute to E region electron heating. It is possible that anomalous electron heating in the auroral electrojet can be explained by (a) the dynamics of those submeter scale waves propagating in the E × B direction (primary waves) or (b) the dynamics of longer wavelengths.
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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 | 0.000 | 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".