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Record W2074376208 · doi:10.1002/2014gl061331

Investigating high‐latitude ionospheric turbulence using global positioning system data

2014· article· en· W2074376208 on OpenAlexaff
H. Mezaoui, A. M. Hamza, P. T. Jayachandran

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsKurtosisSkewnessAmplitudeIonosphereTurbulenceWaveletProbability density functionPhysicsGlobal Positioning SystemGeodesyPhase (matter)GeophysicsComputational physicsGeologyStatistical physicsMeteorologyStatisticsMathematicsOpticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract Statistical properties of the amplitude and phase of GPS L1 signals sampled at 50 Hz are investigated to understand the turbulent behavior of the polar region ionosphere. Wavelet detrended amplitude and phase data are used to construct the probability distribution function (PDF) of the amplitude and phase fluctuations of the signal. Turbulent behavior of the ionosphere is quantified using the skewness and kurtosis of the PDF. It is found that these two independent moments are related through a parabolic relationship, which was also reported in the case of turbulent neutral fluids and turbulent laboratory plasmas.

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 categoriesnone
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 score0.473
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.314
Teacher spread0.257 · 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.

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

Citations19
Published2014
Admission routes1
Has abstractyes

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