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Record W4321995502 · doi:10.5194/egusphere-egu23-10171

Inferred Ionospheric irregularity scales from amplitude scintillation

2023· preprint· en· W4321995502 on OpenAlexaff
K. Meziane, A. M. Hamza, Tayyil P. Jayachandran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsScintillationIonospherePhysicsLatitudeAmplitudeInterplanetary scintillationGeodesyEquatorContext (archaeology)F regionAtmospheric sciencesGeologyGeophysicsOpticsPlasmaSolar windCoronal mass ejection

Abstract

fetched live from OpenAlex

The analysis of the structure function of a GNSS signal amplitude measured on the ground has revealed that ionospheric scintillation could be considered a proxy for ionospheric turbulence. More precisely, and in a recent report, the existence of a linear range with respect to the time lag in the structure function has been highlighted. In this context, the inertial-range analog has been determined from the analysis of a large set of scintillation events collected over several days from Pond Inlet located in the northern polar region and from Sao Paolo located at 23.2 degrees South of the Equator. At high latitude, we found that the mean value of the first-order scaling exponent is H = 0.55 ± 0.07, while a low altitude H is typically larger with H = 0.84 ±.11. This result clearly indicates that the long-time lag positive correlation remains persistent in the low latitude region. At high latitude however, both negative and positive long time lag correlation can occur. In addition, the obtained results clearly show that the inertial range analog is significantly smaller at high latitude, particularly the upper bound time lags at which the structure function deviates from linearity. This distinction may pinpoint to a difference in the ionospheric irregularity drift speed.   

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score1.000

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)0.0000.001

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 head0.036
GPT teacher head0.250
Teacher spread0.214 · 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
Published2023
Admission routes1
Has abstractyes

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