On the consistency of the SuperDARN radar velocity and <b>E</b> × <b>B</b> plasma drift
Bibliographic record
Abstract
Abstract Joint observations of the Rankin Inlet (RKN) and Inuvik Super Dual Auroral Radar Network (SuperDARN) HF radars and Resolute Bay incoherent scatter radar (RISR) are used to assess consistency in their plasma flow velocity measurements. The analysis covers more than 500 h of successful concurrent measurements. We demonstrate that, overall, the radars show close velocities, although there were minor differences including SuperDARN velocity underestimation, in line with previous publications, and the persistent occurrence of measurements with a SuperDARN velocity magnitude above the RISR velocity magnitude. We argue that, for one event, the velocity overestimation occurs owing to echo detection from a laterally refracted RKN beam while, generally, the effect should be fairly wide‐spread in SuperDARN data because of microstructures with enhanced electron density in the scattering volume that might have either weak irregularities or increased local electric fields. We estimate that the correction of RKN velocity data by considering the effect of the index of refraction improves RKN‐RISR velocity agreement but only for 63% of points. This implies that care should be exercised when attempting to correct raw SuperDARN velocity data by the index of refraction.
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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 | 0.003 | 0.007 |
| 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 | 0.000 | 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".