Full field-of-view imaging and multistatic operations for SuperDARN Borealis radars
Bibliographic record
Abstract
Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 monostatic high-frequency (HF, 10-18~MHz) radars which utilise signals scattered from decameter-scale ionospheric irregularities for studying dynamic processes in the ionosphere. By combining line-of-sight velocity measurements of ionospheric scatter echoes from radars with overlapping fields of view, SuperDARN provides maps of ionospheric plasma drift velocity over mid and high latitudes. The conventional SuperDARN radars consecutively scan through sixteen beam directions with dwelling time of 3.5 s/beam, which places a lower limit of one minute to sample the entire field of view. In this work we remove this limitation by utilizing 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 increase the sampling rate of the entire field of view by up to sixteen 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.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".