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Radio-based Sensing of Turbulent, Ionospheric Flow: A Four-dimensional View

2025· article· en· W4414165370 on OpenAlexfundno aff
Magnus F. Ivarsen, Kaili Song, P. T. Jayachandran, Brian Pitzel, Saif Marei, G. C. Hussey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyAlliance de recherche numérique du CanadaEuropean Space Agency
KeywordsIonosphereGlobal Positioning SystemNoise (video)Satellite

Abstract

fetched live from OpenAlex

This talk will present the results from combining two new and fundamentally different radio-based methods to obtain electrodynamic and spectral measurements of ionospheric turbulence, using the coherent scatter radar ICEBEAR, as well as a CHAIN GPS receiver.We compare the two-point correlation function of coherent echoes in the auroral E-region (its spectrum) to the power spectrum of amplitude scintillations observed in ground-based GPS receivers.We also compare the apparent motion of radar echo clusters to the plasma drift speed derived from GPS signal analysis.Whereas the two methods rely on completely different signals (one spatial and one temporal), they nevertheless produce consistent velocity readings of the flowing ionospheric plasma, as well consistent spectral measurements of the internal structuring of that turbulent flow.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.023
GPT teacher head0.221
Teacher spread0.199 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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