A realistic radar data simulator for the Super Dual Auroral Radar Network
Bibliographic record
Abstract
The Super Dual Auroral Radar Network (SuperDARN) is a chain of HF radars for monitoring plasma flows in the high and middle latitude E and F regions of the ionosphere. The targets of SuperDARN radars are plasma irregularities which can flow up to several kilometers per second and can be detected out to ranges of several thousand kilometers. We have developed a simulator which is able to model SuperDARN data realistically. The simulation system comprises four separate parts: model scatterers, model collective properties, a model radar, and post‐processing. Importantly, the simulator is designed using the collective scatter approach which accurately captures the expected statistical fluctuations of the radar echoes. The output of the program can represent either receiver voltages or autocorrelation functions (ACFs) in standard SuperDARN file formats. The simulator is useful for testing and implementation of SuperDARN data processing software and for investigation of how radar data and performance change when the nature of the irregularities or radar operation varies. The companion paper demonstrates the application of simulated data to evaluate the performance of different ACF fitting algorithms. The data simulator is applicable to other ionospheric radar systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".