Velocity of E region SuperDARN echoes and ExB plasma drift
Bibliographic record
Abstract
Rankin Inlet (RKN) SuperDARN radar observations simultaneously with the Resolute Bay Incoherent scatter radar in nearly coinciding beams are considered to investigate the relationship between the velocity of HF echoes and ExB plasma drift component along the RKN beam. We focus on a case of observations roughly along the flow direction on 6 March 2016. We show that, depending on HF operating frequency, the RKN radar detects either E or F region echoes. For the E region echoes and fast flows of 700-1000 m/s, HF velocities are of two types: very slow with speeds below 100 m/s and fast with speeds up to 400 m/s. Velocities of slow echoes can be of opposite polarity at 10 and 12 MHz and not coincide with the ExB drift polarity. Velocities of fast echoes are somewhat larger at 12 MHz as compared to 10 MHz and both are less than the expected ion-acoustic speed of plasma at typical electrojet heights. No strong range (presumably, aspect angle) attenuation effect is noticed in the range profiles of such echoes. We relate the first type of echoes to the neutral wind turbulence while the second type – to the Farley-Buneman (FB) plasma instability processes. Periods have been noticed when E region echoes had speeds of ~ 200 m/s which is well below the ion-acoustic speed and ExB drift component. We hypothesize that these echoes are owing to FB irregularities generated at low electrojet heights. The observations show existence of extended periods when the RKN radar detects F region echoes at 10 MHz and E region echoes at 12 MHz at the same ranges implying that the “transition region/ranges” for E and F region detection is very sensitive to the observational conditions.
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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".