Application of SuperDARN Interferometry for Improved Estimates of Doppler Velocity and Echo Geolocation
Bibliographic record
Abstract
Abstract It has been previously established that the Doppler velocities of F‐region ionospheric echoes observed by the Super Dual Auroral Radar Network (SuperDARN) at high frequencies (HF, 8–20 MHz) are persistently lower than those measured by other instruments at the same locations. This was attributed to the ionospheric refractive index for HF radio waves being noticeably smaller than one. The refractive index values can be obtained in two ways: based on electron density estimates from a co‐located instrument or a model, or by deriving them from SuperDARN elevation angle data. To compare these methods, we considered line‐of‐sight Doppler velocity observations by the Rankin Inlet (RKN) SuperDARN radar and the Resolute Bay Incoherent Scatter Radars (RISR). The velocity data were supplemented by electron density measurements from RISR. The elevation angle data were also used for accurate determination of SuperDARN echo geolocation because the actual ground range to the echo location may significantly differ from that obtained with the conventional SuperDARN models. The RISR Doppler velocity values were used as a reference to the RKN observations via 0.5‐hop and 1.5‐hop propagation paths. Correction by the index of refraction based on both maximum electron density from the RISR and elevation angle data from RKN brought 0.5‐hop data close to the RISR velocity values, with the latter representing a self‐contained approach. However, for 1.5‐hop echoes from the polar cap, the uncorrected SuperDARN velocities exceeded those from RISR. We discuss potential causes of this apparent anomaly.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".