On the Noise Estimation in Super Dual Auroral Radar Network Data
Bibliographic record
Abstract
Abstract The Super Dual Auroral Radar Network (SuperDARN) currently consists of more than thirty high‐frequency (HF, 3–30 MHz) radars covering mid‐latitude to polar regions in both hemispheres. Their major task is to map ionospheric plasma circulation which provides information about the interactions between the solar wind and the near‐Earth's space plasma environment. One of the major factors defining radar data quality is the signal‐to‐noise ratio (SNR), which requires an accurate characterization of the HF noise. The standard SuperDARN data analysis software uses the SNR as part of a set of empirical procedures designed to remove low‐quality data from further analysis. In this study we found that the currently used empirical algorithm systematically underestimates the noise level by up to 40%. Based on comparison of theoretical and observational noise statistics, we resolve this issue by designing and validating a procedure for accurate background noise level estimation. We then propose a simple SNR threshold to replace the existing criteria for excluding low‐quality data. In addition, we show that several aspects of the radar operational regime design, as well as short‐lived anthropogenic radio interference, can adversely affect the quality of the noise estimates at selected radar sites, and we propose ways to mitigate these problems.
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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 | 0.008 | 0.040 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".