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Record W2012735573 · doi:10.1029/2006ja012024

Comparative statistical analysis of storm time activations and sawtooth events

2007· article· en· W2012735573 on OpenAlexaff
T. I. Pulkkinen, Noora Partamies, R. L. McPherron, M. G. Henderson, G. D. Reeves, M. F. Thomsen, H. J. Singer

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSawtooth waveSubstormSolar windMagnetosphereElectrojetGeophysicsGeomagnetic stormPhysicsIonosphereInterplanetary magnetic fieldGeosynchronous orbitThermosphereStormGeostationary orbitAtmospheric sciencesRing currentEarth's magnetic fieldMeteorologyMagnetic fieldAstronomy

Abstract

fetched live from OpenAlex

Statistical properties of storm time magnetospheric activity are examined using superposed epoch analysis. We show that about half of storm time auroral electrojet activations have signatures that are typical of nonstorm substorms, including geostationary orbit injections and magnetic field dipolarizations. Analysis of a separate data set of sawtooth events shows that they have auroral and inner magnetosphere characteristics that are quite similar to those found generally during storm time activity. Hence it is concluded that the sawtooth events do not represent a specific class of magnetospheric activity. Examination of the solar wind and IMF properties showed that about 30% of storm time substorm‐like activations and about 20% of the sawtooth oscillations have associated solar wind or IMF triggers and that triggering is more likely during high solar wind pressure and fluctuating IMF. The solar wind‐magnetosphere coupling efficiency is shown to be independent of the solar wind Mach number or level of IMF fluctuations but dependent on the level of driving; when E Y is small, the ionospheric dissipation, ring current intensification, and geostationary field stretching are relatively larger than when the driving E Y is large.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.355
Teacher spread0.330 · 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.

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

Citations51
Published2007
Admission routes1
Has abstractyes

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