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Record W2618609490 · doi:10.1002/2017ja024255

Observation of subsecond variations in auroral region total electron content using 100 Hz sampling of GPS observables

2017· article· en· W2618609490 on OpenAlexafffund
Anthony M. McCaffrey, P. T. Jayachandran

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation FoundationArmstrong Foundation
KeywordsTECAmplitudeTotal electron contentIonosphereJitterPhysicsSampling (signal processing)Magnitude (astronomy)Computational physicsPhase (matter)GeodesyAtmospheric sciencesGeophysicsAstrophysicsOpticsGeologyDetectorTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Abstract First ever auroral region total electron content (TEC) measurements at 100 Hz using a Septentrio PolaRxS Pro receiver are analyzed to discover ionospheric signatures which would otherwise be unobtainable with the frequently used lower sampling rates. Two types of variations are observed: small‐magnitude (amplitude) variations, which are present consistently throughout the data set, and larger‐magnitude (amplitude) variations, which are less frequent. Small‐amplitude TEC fluctuations are accounted for by the receiver phase jitter. However, estimated secondary ionospheric effects in the calculation of TEC and the receiver phase jitter were unable to account for the larger‐amplitude TEC fluctuations. These variations are also accompanied by fluctuations in the magnetic field, which seems to indicate that these fluctuations are real and of geophysical significance. This paper presents a technique and the capability of high‐rate TEC measurements in the study of auroral dynamics. Further detailed study is needed to identify the cause of these subsecond TEC fluctuations and associated magnetic field fluctuations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.176
GPT teacher head0.376
Teacher spread0.200 · 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 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

Citations10
Published2017
Admission routes2
Has abstractyes

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