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Record W2036240937 · doi:10.1029/2011ja017210

Geostationary magnetic field response to solar wind pressure variations: Time delay and local time variation

2012· article· en· W2036240937 on OpenAlexafffund
B. J. Jackel, Breege McKiernan, H. J. Singer

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNoonSolar windMagnetosphereDynamic pressureMagnetopauseEnvironmental scienceAtmospheric sciencesGeosynchronous orbitGeostationary orbitPhysicsCoronal mass ejectionInterplanetary magnetic fieldSolar maximumSatelliteMagnetic fieldAstronomyMechanics

Abstract

fetched live from OpenAlex

The relationship between solar wind dynamic pressure changes and geosynchronous magnetic field response is studied using 15 years of OMNI2 and GOES data at 1‐minute resolution. Significant magnetospheric response to solar wind‐forcing is found to be most frequent near noon (30% of all intervals), and virtually absent on the night‐side. The strongest response occurs when IMF Bz is strongly northward and the effect of reducing IMF Bz is most pronounced in the dusk sector. Approximately 25% of dayside Bz variance for related intervals can be attributed to direct response from solar wind dynamic pressure forcing. Time lag between changes in the solar wind at the bow shock nose and similar fluctuations in the magnetosphere at geosynchronous orbit (6.6 Re) is typically 2 to 4 minutes, with responses occurring first in the post‐noon sector and approximately 2 minutes later near dawn. The OMNI2 HRO time‐shifting algorithm appears to be quite effective, with a slight (2 minute) systematic increase in lag and no increased scatter for the most distant upstream solar wind satellite location.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.273
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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
Published2012
Admission routes2
Has abstractyes

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