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Record W3017983685 · doi:10.1029/2019ja027160

A Characterization of Periodicity in the Voltage Time Series of a Riometer

2020· article· en· W3017983685 on OpenAlexafffundabout
Francois A. Marshall, David J. Thomson, Alan D. Chave, Robyn Fiori, D. W. Danskin

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsNatural Resources CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsRiometerPhysicsComputational physicsSeries (stratigraphy)Spectral densityAmplitudeVoltageHarmonicNoise (video)Spectral lineSidereal timeAstrophysicsMathematicsOpticsIonosphereAcousticsGeophysicsGeologyStatistics

Abstract

fetched live from OpenAlex

Abstract This paper reveals unprecedented periodicity in the voltage series of relative ionospheric opacity meters (riometers) of the Canadian Riometer Array (CRA). In quiet times, the riometer voltage series is accurately modeled by a stochastic process whose components include both a six term expansion in harmonic functions and some amplitude modulated modes of lower signal to noise ratio (SNR). In units of cycles per sidereal day (cpsd), the frequencies of the six harmonic functions lie within 0.01 cpsd of an integer. Earth's rotation induces a splitting of the low SNR components, resulting in the appearance of nine multiplets in standardized power spectrum estimates of the considered CRA voltage series. A second feature of these spectrum estimates is a 6 min periodic element appearing in both the CRA voltage series and the proton mass density series of the Advanced Composition Explorer (ACE). Spectral peak frequencies have been detected, which lie near established solar mode frequency estimates. In addition, some of these peak frequency estimates are coincident with peak frequency estimates of the standardized power spectra for the time series of proton mass density and interplanetary magnetic field strength (IMF) at ACE.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.030
GPT teacher head0.299
Teacher spread0.269 · 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 designBench or experimental
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
Published2020
Admission routes3
Has abstractyes

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