The Fast Borealis Ionosphere: New observations and insights from mapping the polar ionosphere every four seconds
Bibliographic record
Abstract
For the past three decades, ionospheric drift velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) have been combined at a nominal time resolution of two minutes to produce horizontal patterns of the high-latitude convective flow. Recently, SuperDARN radars operated by the University of Saskatchewan (codenamed Borealis), which overlook much of the northern hemisphere polar cap, have been upgraded to enable a form of scanning which can be carried out every 3.7 seconds without compromising on the large field-of-views of the radars. When data from all Borealis radars are combined, a 32-fold temporal resolution improvement over traditional SuperDARN convection maps is achieved. We call this new data product the Fast Borealis Ionosphere (FBI).The SuperDARN FBI allows for the study of highly transient and quickly evolving ionospheric phenomena (or the order of seconds) that span several thousands of kilometres, such as transient flow bursts, polar cap patches, substorm-related enhancements, and more. In this presentation, we show FBI results for events highlighting its capabilities in capturing transient ionospheric dynamics, along with several conjunction studies with satellites and other ground-based instruments (such as all-sky cameras).
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".