The Properties of ICEBEAR E‐Region Coherent Radar Echoes in the Presence of Near Infrared Auroral Emissions, as Measured by the Swarm‐E Fast Auroral Imager
Bibliographic record
Abstract
Abstract For the first time, near infrared (NIR) auroral emissions measured from space have been compared with E‐region coherent scatter. The E‐region coherent scatter observations were obtained by the 49.5 MHz Ionospheric Continuous‐wave E‐region Bistatic Experimental Auroral Radar (ICEBEAR) in western Canada. NIR emissions integrated over 650–1,100 nm wavelengths were obtained from the Fast Auroral Imager instrument onboard e‐POP on CASSIOPE (now Swarm‐E) with a 1 s temporal resolution (same as ICEBEAR), with the instrument slewed to the center of the ICEBEAR field‐of‐view. The coherent echoes and the NIR emissions are expected to originate below 120 km altitude. The radar spectra indicated that the turbulence was very weak. The location of all coherent echoes corresponded to NIR emission brightness values of more than 250 kR (kilo‐Rayleighs) and less than 1,250 kR. When the emissions were very bright, the electric fields were seemingly too weak to produce plasma instabilities, explaining why no radar echoes were detected. By contrast, even if the electric field happened to be relatively strong in dark regions, the background plasma densities were too small to generate detectable coherent scatter radar echoes. The Doppler shifts of the coherent scatter spectra were clustered around 465 and ± 250 m/s. These magnitudes did not agree with the quiet 350 m/s ion‐acoustic speed expected from the weaker electric fields that should have prevailed under weak turbulence situations. The unexpected Doppler shifts were potentially due to either unexpected strong electric fields and altitude variations, or ambient km‐size density gradients at 110 km.
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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 | 0.000 | 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".