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Record W2005714393 · doi:10.1002/2014ja020419

Magnetic field power spectra and magnetic radial diffusion coefficients using CRRES magnetometer data

2015· article· en· W2005714393 on OpenAlexaff
S. R. Elkington, Weichao Tu, L. G. Ozeke, A. A. Chan, R. H. W. Friedel

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Aeronautics and Space Administration
KeywordsMagnetometerNoonEarth's magnetic fieldPhysicsMagnetic fieldDiffusionSpectral densityVan Allen radiation beltSpectral lineMagnetosphereWavenumberComputational physicsAstrophysicsAtmospheric sciencesOpticsAstronomy

Abstract

fetched live from OpenAlex

Abstract We used the fluxgate magnetometer data from Combined Release and Radiation Effects Satellite (CRRES) to estimate the power spectral density (PSD) of the compressional component of the geomagnetic field in the ∼1 mHz to ∼8 mHz range. We conclude that magnetic wave power is generally higher in the noon sector for quiet times with no significant difference between the dawn, dusk, and the midnight sectors. However, during high K p activity, the noon sector is not necessarily dominant anymore. The magnetic PSDs have a very distinct dependence on K p . In addition, the PSDs appear to have a weak dependence on McIlwain parameter L with power slightly increasing as L increases. The magnetic wave PSDs are used along with the Fei et al. (2006) formulation to compute as a function of L and K p . The L dependence of is systematically studied and is shown to depend on K p . More significantly, we conclude that is the dominant term driving radial diffusion, typically exceeding by 1–2 orders of magnitude.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.967

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 communication0.0000.001
Open science0.0010.001
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 head0.052
GPT teacher head0.343
Teacher spread0.291 · 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 designOther design
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

Citations33
Published2015
Admission routes1
Has abstractyes

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