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Record W1592226826 · doi:10.1029/2009gl042143

Thermospheric density enhancements in the dayside cusp region during strong B<sub>Y</sub> conditions

2010· article· en· W1592226826 on OpenAlexfundno aff
G. Crowley, D. J. Knipp, K. A. Drake, Jiuhou Lei, E. K. Sutton, H. Lühr

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchMultidisciplinary University Research InitiativeUniversity of Texas at DallasUniversity of AlbertaUniversity of Texas at San AntonioScience and Technology Facilities CouncilJohns Hopkins UniversityUniversity of MichiganNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsThermosphereCusp (singularity)IonosphereLatitudePhysicsAtmospheric sciencesGeophysicsComputational physicsAstronomyGeometry

Abstract

fetched live from OpenAlex

Tri‐axial accelerometer data from the Challenging Minisatellite Payload (CHAMP) satellite have revealed the thermospheric density and its variability in unprecedented detail. The data often contain regions of high density located in the cusp sector at high latitudes. In this paper we provide the first detailed explanation of a high latitude density enhancement observed by CHAMP, focusing on the August 24, 2005 interval. The Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM) was driven by high‐fidelity high‐latitude inputs specified by the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) algorithm, and reproduced the main features of the density enhancements. The TIMEGCM and AMIE provide a global framework for interpretation of the CHAMP densities. Our simulations reveal that the observed density enhancement in the dayside cusp region resulted from unexpectedly large amounts of energy entering the Ionosphere‐Thermosphere system at cusp latitudes during an interval of strong (+20 nT) B Y .

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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 designBench or experimental
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

Citations85
Published2010
Admission routes1
Has abstractyes

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