ICEBEAR: An All‐Digital Bistatic Coded Continuous‐Wave Radar for Studies of the <i>E</i> Region of the Ionosphere
Bibliographic record
Abstract
Abstract The Ionospheric Continuous‐wave E region Bistatic Experimental Auroral Radar (ICEBEAR) is a coherent scatter ionospheric radar. It operates at a frequency of 49.5 MHz, which is ideal for observing E region coherent echoes. The radar is located in Saskatchewan, Canada, and is operated by the University of Saskatchewan. The ICEBEAR system uses a continuous‐wave (CW) signal and requires isolation between the receiving and transmitting arrays. This was accomplished through a bistatic setup, where the receiver and transmitter are ≈240 km apart. Currently, the ICEBEAR system implements a pseudo random noise phase modulation on this CW signal to obtain 3‐km range resolution and 5‐s integration time images of E region ionospheric irregularities over a 600 km × 600 km field of view. The center of the field of view is located at ≈58°N, 106°W. The radar design allows for future improvements to temporal and/or spatial resolutions. Each site consists of a linear phased array with 10 equally spaced antennas. This, combined with modern digital radio hardware, provides azimuthal angle of arrival measurements at the receiving array and azimuthal transmission control at the transmitting array. This publication describes the radio hardware and signal processing used by the ICEBEAR radar and emphasizes the unique capabilities of the radar. First ICEBEAR observations from a Kp ≥ 4 event on 10 March 2018, are presented and shown to produce simultaneously the four types of previously characterized E region coherent scatter echoes.
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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 | 0.000 |
| 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 | 0.001 |
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".