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Record W2126791242 · doi:10.1029/2003rs002975

Ionospheric structure effects on HF radio wave propagation for the Enhanced Polar Outflow Probe (e‐POP) satellite mission

2004· article· en· W2126791242 on OpenAlexaffabout
L. Wang, J. W. MacDougall, H. G. James

Bibliographic record

VenueRadio Science · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsCommunications Research Centre CanadaWestern University
Fundersnot available
KeywordsIonosphereIonosondeOutflowHigh frequencySatelliteRadio waveGeophysicsGeologyDoppler effectRay tracing (physics)PhysicsGeodesyRemote sensingPlasmaMeteorologyOpticsElectron densityAstronomy

Abstract

fetched live from OpenAlex

The Enhanced Polar Outflow Probe (e‐POP) payload will be launched on a small satellite in 2007 for exploring plasma and atmospheric outflow process in the polar region. The subject of this paper is whether one can determine the properties of large‐scale ionospheric structures by studying the perturbations that they cause on HF radio waves received at the e‐POP satellite from ground‐based transmitters such as the Canadian Advanced Digital Ionosonde (CADI). The perturbations on the received waves have been investigated using numerical ray‐tracing methods. These simulation results show that ionospheric irregular structures lead to a complex pattern of amplitude, propagation time delay, Doppler frequency, and direction‐of‐arrival (DOA) effects on the HF radio waves received at the satellite. The simulations also show that ionospheric density structure cannot be measured unambiguously using a single wave property. Therefore a “catalog” of HF signatures of typical ionospheric irregularities has been established in order to be able to interpret the e‐POP HF measurements in terms of ionospheric structures.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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 designObservational
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

Citations9
Published2004
Admission routes2
Has abstractyes

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