High time resolution mapping of polar ionospheric flows with the SuperDARN Borealis systems
Bibliographic record
Abstract
Over the last few years, the five SuperDARN HF ionospheric radars operated by the University of Saskatchewan have been upgraded to digital systems that utilise the flexibility and reliability of software defined radios (SDRs). SDRs allow for a vastly greater control of radar transmit and receive operations, bringing with them new capabilities for scientific experiments that were previously not feasible on analogue hardware. This next generation of SuperDARN radar is named Borealis. One new radar operating mode implemented at the Borealis radars has been full field-of-view imaging. On traditional SuperDARN radars, one full scan of an entire field-of-view (an area encompassing thousands of kilometres at F-region ionospheric altitudes) takes approximately 1 minute as each of the 16 beam directions is sequentially integrated over. With Borealis, every beam direction can be probed (or “imaged”) simultaneously, providing a 16-fold improvement in scan temporal resolution to 3.5 seconds. We present a new ionospheric data product derived from Borealis imaging mode data: high time resolution mapping of polar E x B drifts. In contrast to traditional SuperDARN ionospheric convection patterns which are nominally derived every two minutes on a coarse global grid, Borealis convection patterns are derived locally over the Canadian polar cap every few seconds. This not only provides the opportunity to study mesoscale ionospheric phenomena like polar cap patches, flow channels, and substorms, but also allows for doing so at a temporal resolution not previously possible without compromising spatial coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".