Velocity of SuperDARN Echoes at Intermediate Radar Ranges
Bibliographic record
Abstract
Abstract The study investigates the relationship between Super Dual Auroral Radar Network (SuperDARN) high‐frequency (HF) radar velocities measured at intermediate ranges of 600–1,000 km from the radar and the E × B plasma drift. Two approaches are implemented. First, a 3‐h interval of SuperDARN Rankin Inlet (RKN) radar measurements and Resolute Bay incoherent scatter radar RISR‐C measurements in nearly coinciding directions is investigated to show that (1) HF echoes with low velocities (less than 200 m/s) are often detected when E × B drifts are in excess of 1,000 m/s; (2) high‐velocity HF echoes from the E region have velocities somewhat below the expected values of the ion‐acoustic speed of the plasma and the HF line‐of‐sight velocity does not show a tendency for an increase at the largest E × B drifts; (3) for E region echoes, 12 MHz velocities are slightly larger than those at 10 MHz; and (4) 12‐MHz echoes are often received from the electrojet heights while 10‐MHz echoes are received from the F region heights so that the observed velocities are quite different with the latter reflecting the E × B drift of the plasma. In the second approach, velocities of 10‐ and 12‐MHz RKN echoes are compared for a large data set comprising several months of observations to show that occurrence of 12‐MHz low‐velocity echoes is fairly common (up to 25% of the time) whenever the plasma drifts are fast. Under this condition, the SuperDARN cross polar cap potential is underestimated, on average, by ~4 kV.
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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 | 0.001 |
| 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 | 0.000 | 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".