ICEBEAR: Recent Results from a Bistatic Coded Continuous-Wave E-region Coherent Scatter Radar
Bibliographic record
Abstract
The Ionospheric Continuous-wave E-region Bistatic Experimental Auroral Radar (ICEBEAR) is located in Canada and has a field of view centered at (58°N, 106°W) overlooking the terrestrial auroral zone. This 49.5 MHz coherent scatter radar measures plasma density irregularities in the E-region ionosphere using a pseudo random noise phase modulated continuous-wave (CW) signal. ICEBEAR uses this coded CW signal to obtain simultaneous high temporal (1 s) and spatial (1.5 km) resolutions of E-region plasma density turbulence over a 600 km x 600 km field of view, providing insights into the Farley-Buneman plasma density instability and wave-like structures evident in the coherent scatter. The initial results from ICEBEAR were obtained with a 1D receiving array, providing azimuthal angle of arrival details of the incoming scattered signal. This azimuthal determination, along with the range determined using the coded signal, allowed the scatter to be mapped in 2D. A recent reconfiguration of the receiving array has allowed the elevation angle of the received signal to be calculated, providing 3D determination of the location of the plasma density irregularities. This presentation will demonstrate the capabilities of ICEBEAR, displaying measurements of highly dynamic plasma density irregularities with wave-like behaviour on 1 second time scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".