Stereo CUTLASS - A new capability for the SuperDARN HF radars
Bibliographic record
Abstract
Abstract. The Super Dual Auroral Radar Network (SuperDARN), which consists of networks of HF radars surrounding the northern and southern poles, has proven to be an extremely successful experimental technique in a wide range of scientific areas. The basic design of the radars, which contributes to SuperDARN, has remained virtually unchanged since the first radar was built in the early 1980s. This paper presents the first results of a significant new development of the basic system. Termed "Stereo", this development involves the addition of a duplicate receive channel and makes use of the spare duty cycle available in the current transmitters. The Stereo system has been implemented on the two radars that form the Co-operative UK Twin Located Auroral Sounding System (CUTLASS), which are the easternmost pair of radars in the Northern Hemisphere SuperDARN chain. Instead of the standard 7 pulse sequence normally employed by the radars, two such pulse sequences are interleaved and transmitted at different frequencies separated by more than 15kHz. This development allows for the radar to run two entirely different experimental modes simultaneously. Here we describe the basic Stereo system and some of the early results. We also identify potential new experiments which could be run with Stereo to complement the existing standard SuperDARN radar. Key words. Ionosphere (ionosphere-magnetosphere interactions; ionospheric irregularities; instruments and techniques)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".