Application of Wide‐Beam Transmission for Advanced Operations of SuperDARN Borealis Radars in Monostatic and Multistatic Modes
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 monostatic high‐frequency (HF, 8–20 MHz) radars to study dynamic processes in the ionosphere. SuperDARN provides maps of global‐scale ionospheric plasma drift circulation from the mid‐latitudes to the poles. The conventional SuperDARN radars consecutively scan through 16 beam directions with a lower limit of 1 minute to sample the entire field of view. In this work, we use the advanced capabilities of the recently developed Borealis digital SuperDARN radar system. Combining a wide transmission beam with multiple narrow reception beams allows us to sample all conventional beam directions simultaneously and to speed up scanning of the entire field‐of‐view by up to 16 times without noticeable deterioration of the data quality. The wide‐beam emission also enabled the implementation of multistatic operations, where ionospheric scatter signals from one radar are received by other radars with overlapping viewing areas. These novel operations required the development of a new model to determine the geographic location of the source of the multistatic radar echoes. Our preliminary studies showed that, in comparison with the conventional monostatic operations, the multistatic operations provide a significant increase in geographic coverage, in some cases nearly doubling it. The multistatic data also provide additional velocity vector components, increasing the likelihood of reconstructing full plasma drift velocity vectors. The developed operational modes can be readily implemented at other fully digital SuperDARN radars.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it