Determination of the Azimuthal Extent of Coherent E‐Region Scatter Using the ICEBEAR Linear Receiver Array
Bibliographic record
Abstract
Abstract The Ionospheric Continuous‐wave E‐region Bistatic Experimental Auroral Radar (ICEBEAR) is a VHF coherent scatter radar that operates with a field‐of‐view centered on 58°N, 106°W and measures characteristics of ionospheric E‐region plasma density irregularities. The initial operations of ICEBEAR utilized a wavelength‐spaced linear receiving array to determine the angle of arrival of the ionospheric scatter at the receiver site. Initially only the shortest baselines were used to determine the angle of arrival of the scatter. This publication uses this linear antenna array configuration and expands on the initial angle of arrival determination by including all the cross‐spectra available from the antenna array to determine both the azimuthal angle of arrival and the azimuthal extent of the incoming ionospheric scatter. This is accomplished by fitting Gaussian distributions to the complex coherence of the signal between different antennas and deriving the azimuthal angle and extent based on the best fit. Fourteen hours of data during an active ionospheric period (March 10, 2018, 0–14 UT) were analyzed to investigate the Gaussian fitting procedure and determine its feasibility for implementation with ICEBEAR. A comparison between mapped scatter, both neglecting azimuthal extent and including azimuthal extent is presented. It demonstrates that the azimuthal extent of the ionospheric E‐region scatter is very important for accurately portraying and analyzing the ICEBEAR measurements.
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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 | 0.001 | 0.001 |
| 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".