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Record W4322011360 · doi:10.5194/egusphere-egu23-10103

The highly advanced ICEBEAR-3D E-region coherent imaging radar

2023· preprint· en· W4322011360 on OpenAlexaffabout
G. C. Hussey, Adam Lozinsky, Brian Pitzel, Magnus F. Ivarsen, Draven Galsechuk, Devin Huyghebaert, K. A. McWilliams, J.‐P. St.‐Maurice

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInterferometryRemote sensingRadarRadar imagingBistatic radarAntenna (radio)AzimuthGeologyIonosphereSynthetic aperture radarSide looking airborne radarPhysicsGeodesyOpticsComputer scienceTelecommunicationsGeophysics

Abstract

fetched live from OpenAlex

ICEBEAR (Ionospheric Continuous-wave E-region Bistatic Experimental Auroral Radar) employs advanced software defined radio (SDR) and aperture synthesis radar imaging to produce unambiguous high resolution (1--2~km) 3-dimensional (range, azimuth, elevation) coherent E-region observations. ICEBEAR-3D operates in the VHF at 49.5~MHz observing the auroral zone of the ionosphere from western Canada (58N, 106W geographic). The receiver antenna array was re-configuration in 2019 to a non-uniform co-planar T-shaped double interferometer layout to complete the ICEBEAR design and allow for unambiguous, highly detailed, high-resolution coherent radar E-region observations. We present the antenna array re-configuration; the novel and advanced synthesis aperture radar imaging technique; a low elevation angle accuracy and reliability solution; validations and calibrations of ICEBEAR-3D using celestial radio sources (Cygnus A) and interferometer closure angles; as well as some initial E-region and meteor trail observations and analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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