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Radio Wave Propagation Revisited with Application to High-Latitude Ionospheric Scintillation

2025· preprint· en· W4411787033 on OpenAlexafffund
A. M. Hamza, K. Meziane

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScintillationInterplanetary scintillationIonosphereHigh latitudeRadio propagationRadio waveLatitudeGeologyGeophysicsPhysicsEnvironmental scienceComputer scienceAstronomyGeodesyTelecommunicationsOpticsPlasmaNuclear physics

Abstract

fetched live from OpenAlex

The subject of radio-wave propagation in various media, including turbulent plasmas, has for more than a century preoccupied astronomers, space plasma physicists, scientists and engineers interested in the problem of wave scattering by turbulent media. We are particularly interested in the propagation of radio waves in the high-latitude ionosphere and the wave-scintillation signature as recorded by ground-based instruments. We first start by reviewing the universal problem of wave propagation in plasmas. We then identify the main approximations to be adopted to reduce the complex problem of wave propagation in a turbulent medium to a nonlinear wave equation. A new approach, based on analytical tools borrowed from quantum mechanics and statistical mechanics, is proposed to derive statistical properties of scintillation signals recorded by ground based instruments located in both the auroral and polar cap regions.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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