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Record W2136979668 · doi:10.5194/angeo-27-1717-2009

Studies of magnetotail dynamics and energy evolution during substorms using MHD simulations

2009· article· en· W2136979668 on OpenAlexfundaboutno aff
Sandra Brogl, Ramón López, M. Wiltberger, H. K. Rassoul

Bibliographic record

VenueAnnales Geophysicae · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersCanadian Space AgencyUniversity of AlbertaNational Center for Atmospheric ResearchNational Science Foundation
KeywordsSubstormPlasma sheetPhysicsGeophysicsMagnetometerPlasmaPolarMagnetosphereMagnetohydrodynamicsFlux (metallurgy)Current sheetMagnetic fieldAtmospheric sciencesGeologyComputational physicsMaterials scienceAstronomyNuclear physics

Abstract

fetched live from OpenAlex

Abstract. We examine the distribution and propagation of energy in the plasma sheet and lobes using observations and simulations for three substorms. The substorms occurred on 9 March 1995, 10 December 1996, and 27 August 2001 and have been simulated using the Lyon-Fedder-Mobarry magneto-hydrodynamic code. All three events occur over North America and show a clear substorm current wedge over the ground magnetometer chains of Alaska, Canada, and Greenland. The three simulations show the thinning of the plasma sheet during the growth phase of the event and an increase in the relative amount of thermal energy due to the compression of the plasma sheet. Generally, the total lobe energy, polar cap flux, and lobe magnetic field strength simultaneously increase during the growth phase, and polar cap flux and total lobe energy only start dropping at substorm onset, as measured by the CANOPUS magnetometer chain. Starting at time of onset and continuing throughout the expansion phase a transfer of magnetic energy from the lobes into the plasma sheet occurs, with the increase in the plasma sheet energy ranging from 30–40% of the energy that is released from the lobes.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.657

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 head0.011
GPT teacher head0.246
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 designTheoretical or conceptual
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

Citations3
Published2009
Admission routes2
Has abstractyes

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