A statistical analysis of SuperDARN scattering volume electron densities and velocity corrections using a radar frequency shifting technique
Bibliographic record
Abstract
Ionospheric plasma drift velocities measured by High Frequency (HF) coherent scatter radars, such as the Super Dual Auroral Radar Network (SuperDARN), are typically underestimated, sometimes significantly, because the refractive index in the scattering volume is not known. Large‐scale or background estimates of ionospheric electron density and refractive index can be made by other instruments; however, these instruments both do not cover the large field‐of‐view of the SuperDARN radars and do not provide information about the small‐scale structures which may be very important for the scattering process. A method has been developed to use different operating frequencies of the SuperDARN radars to obtain the average scattering volume electron density. These electron density measurements provide an estimate of refractive index and allow for corrections to the SuperDARN velocity data to be made. A comprehensive analysis of all SuperDARN data since its inception almost 20 years ago has provided estimates of average electron density in the scattering volume of the radars for various magnetic latitudes, solar activities, local times, and seasons. The analysis indicates that the average electron density, and therefore refractive index, in the scattering volume can vary significantly with the various parameters. Densities ranging from less than 2 × 1011 m−3 to more than 8 × 1011 m−3, result in refractive index corrections from less than 5% (not very significant) to more than 50% (extremely significant). These results provide estimates of appropriate adjustments to the drift velocities assumed by SuperDARN for various conditions. Further, this research has provided substantial insight into the physics of the coherent scattering process and provides a method by which electron density of the scattering structures can be monitored. This will be tested using in situ high‐latitude ionospheric measurements from the upcoming enhanced Polar Outflow Probe (ePOP) satellite mission.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".