Bibliographic record
Abstract
Abstract Accurate geolocation of ionospheric backscatter measured by the Super Dual Auroral Radar Network (SuperDARN) high‐frequency radars is critical for the integrity of polar ionospheric convection maps, which involve combining SuperDARN line‐of‐sight velocity measurements originating from multiple locations. Geolocation requires estimation of the propagation paths of the high‐frequency radio signal to and from the scattering volume. The SuperDARN radars comprise both a main and interferometer antenna array to allow the estimation of the elevation angle of arrival of the returning signal, and hence its most likely propagation path. However, over the history of operation of SuperDARN (>20 years) elevation angle data have not been routinely used owing to problems with the calibration of phase difference measurements. Instead, virtual height models have been used to estimate the most likely propagation paths, and these are often of limited accuracy. Here we present a method for calibrating SuperDARN interferometer measurements using backscatter from meteor trails measured in the near field‐of‐view of the SuperDARN radars. We present estimates of calibration factors for the SuperDARN radar in Saskatoon, Canada, at different temporal resolutions: 3 months, 10 days, and 1 day. The calibration factor varies over the 9‐year interval studied, such that employing a single value for the whole interval would lead to significant errors in elevation angle measurements at times. The higher‐resolution results show the ability of the technique to determine the calibration factor routinely at a high time resolution.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".