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Record W4282549863 · doi:10.1186/s40623-022-01642-1

Evidence for presence of a global quasi-resonant mode of oscillations during high-intensity long-duration continuous AE activity (HILDCAA) events

2022· article· en· W4282549863 on OpenAlexafffund
Diptiranjan Rout, Ram Singh, Kuldeep Pandey, Tarun Kumar Pant, Claudia Stolle, D. Chakrabarty, Smitha V. Thampi, Tikemani Bag

Bibliographic record

VenueEarth Planets and Space · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Space AgencyAlexander von Humboldt-Stiftung
KeywordsPhysicsGeosynchronous orbitIonosphereSolar windMagnetosphereEarth's magnetic fieldGeophysicsOscillation (cell signaling)Interplanetary magnetic fieldIonospheric dynamo regionGeomagnetic stormMagnetopauseAtmospheric sciencesComputational physicsMagnetic fieldSatelliteAstronomy

Abstract

fetched live from OpenAlex

Abstract The responses of two High-Intensity Long-Duration Continuous AE Activity (HILDCAA) events are investigated using solar wind observations at L1, magnetospheric measurements at geosynchronous orbit, and changes in the global ionosphere. This study provides evidence of the existence of quasi-periodic oscillations (1.5–2 h) in the ionospheric electric field over low latitudes, total electron content at high latitudes, the magnetic field over the globe, energetic electron flux and magnetic field at geosynchronous orbit, geomagnetic indices (SYM-H, AE, and PC) and the Y-component of the interplanetary electric field (IEFy) during the HILDCAA events at all local times. Based on detailed wavelet and cross-spectrum analyses, it is shown that the quasi-periodic oscillation of 1.5–2 h in IEFy is the most effective one that controls the solar wind–magnetosphere–ionosphere coupling process during the HILDCAA events for several days. Therefore, this investigation for the first time, shows that the HILDCAA event affects the global magnetosphere–ionosphere system with a “quasi-resonant” mode of oscillation. Graphical Abstract

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.498

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)0.0000.000

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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

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