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Record W2015034201 · doi:10.1029/2009ja014673

Effects of substorm dynamics on magnetic signatures of the ionospheric Alfvén resonator

2010· article· en· W2015034201 on OpenAlexaff
A. Parent, I. R. Mann, I. J. Rae

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSubstormIonosphereRiometerPhysicsGeophysicsElectron precipitationEarth's magnetic fieldAtmospheric sciencesComputational physicsMagnetosphereMagnetic field

Abstract

fetched live from OpenAlex

Spectral resonance structures (SRS) of the ionospheric Alfvén resonator (IAR) measured by the induction magnetometer at the High Frequency Active Auroral Research Program (HAARP) ionospheric observatory during a substorm on 28 February 2006 are presented. The evolution of IAR SRS is compared to ionospheric parameters measured by the colocated Digisonde, riometer and all‐sky imager at HAARP. Initially, the magnetic IAR signatures (spectral resonance structures) exhibited an expected variation that can be attributed to typical diurnal changes in ionospheric structure. At substorm onset, the signatures disappeared because of either a suppression of resonance conditions by substorm‐related particle precipitation or enhanced power in the Pc1 spectrum that concealed continuing IAR SRS. After the substorm, the SRS reappeared; however the harmonics had shifted to lower frequencies with tighter frequency spacing. For the first time, we show that this time‐dependent behavior in IAR SRS is explained by increased F region densities resulting from electron precipitation. Similarities between observed IAR harmonic frequencies and those calculated with a model suggest that variations in F region density, especially f o F 2 , may often dominate the evolution of IAR eigenfrequencies. This could potentially provide a mechanism for monitoring topside dynamics using IAR SRS.

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 score0.555
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.259
Teacher spread0.253 · 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

Citations34
Published2010
Admission routes1
Has abstractyes

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